Beacon-based distributed data processing platform

ABSTRACT

A method comprises initiating a first application in a first one of a plurality of distributed processing nodes, and responsive to initiation of the first application, identifying a plurality of beacon entities to be contacted in conjunction with execution of at least a portion of the first application. The method also comprises, for each of at least a subset of the identified beacon entities, initiating an additional application in an additional one of the plurality of distributed processing nodes. The method further comprises aggregating processing results from the first and one or more additional processing nodes, and providing the aggregated processing results to a client. The plurality of distributed processing nodes may comprise a plurality of YARN clusters associated with respective data zones, with each of the clusters being configured to perform processing operations utilizing local data resources locally accessible within its corresponding data zone.

PRIORITY CLAIM

The present application is a continuation of U.S. patent applicationSer. No. 14/982,355, filed Dec. 29, 2015, which claims priority to U.S.Provisional Patent Application Ser. No. 62/143,404, entitled “World WideHadoop Platform,” and U.S. Provisional Patent Application Ser. No.62/143,685, entitled “Bioinformatics,” both filed Apr. 6, 2015, andincorporated by reference herein in their entirety.

RELATED APPLICATIONS

The present application is related to U.S. patent application Ser. No.14/982,341, entitled “Multi-Cluster Distributed Data ProcessingPlatform,” and U.S. patent application Ser. No. 14/982,351, entitled“Distributed Catalog Service for Multi-Cluster Data ProcessingPlatform,” each of which is incorporated by reference herein in itsentirety.

FIELD

The field relates generally to information processing systems, and moreparticularly to information processing systems that implementdistributed processing across a plurality of processing nodes.

BACKGROUND

The need to extract knowledge from data collected on a global scalecontinues to grow. In many cases the data may be dispersed acrossmultiple geographic locations, owned by different entities, and indifferent formats. Although numerous distributed data processingframeworks exist today, these frameworks have significant drawbacks. Forexample, data-intensive computing tasks often use data processingframeworks such as MapReduce or Spark. However, these frameworkstypically require deployment of a distributed file system shared by allof the processing nodes, and are therefore limited to data that isaccessible via the shared distributed file system. Such a shareddistributed file system can be difficult to configure and maintain overmultiple local sites that are geographically dispersed and possibly alsosubject to the above-noted differences in ownership and data format. Inthe absence of a shared distributed file system, conventionalarrangements may require that data collected from sources in differentgeographic locations be copied from their respective local sites to asingle centralized site configured to perform data analytics. Such anarrangement is not only slow and inefficient, but it can also raiseserious privacy concerns regarding the copied data.

SUMMARY

Illustrative embodiments of the present invention provide informationprocessing systems that are configured to execute distributedapplications over multiple distributed data processing node clustersassociated with respective distinct data zones. Each data zone in agiven embodiment illustratively comprises a Hadoop YARN clusterconfigured to support multiple distributed data processing frameworks,such as MapReduce and Spark. These and other similar arrangementsdisclosed herein can be advantageously configured to provide analyticsfunctionality in a decentralized and privacy-preserving manner, so as toovercome the above-noted drawbacks of conventional systems. This isachieved in some embodiments by orchestrating execution of distributedapplications across the multiple YARN clusters. Computations associatedwith data available locally within a given YARN cluster are performedwithin that cluster. Accordingly, instead of moving data from localsites to a centralized site, computations are performed within the localsites where the needed data is available. This provides significantadvantages in terms of both performance and privacy. Additionaladvantages are provided in terms of security, governance, risk andcompliance.

In one embodiment, a method comprises initiating a first application ina first one of a plurality of distributed processing nodes, andresponsive to initiation of the first application, identifying aplurality of beacon entities to be contacted in conjunction withexecution of at least a portion of the first application. The methodalso comprises, for each of at least a subset of the identified beaconentities, initiating an additional application in an additional one ofthe plurality of distributed processing nodes. The method furthercomprises aggregating processing results from the first and one or moreadditional processing nodes, and providing the aggregated processingresults to a client.

The plurality of distributed processing nodes may comprise a pluralityof YARN clusters associated with respective data zones, with each of theclusters being configured to perform processing operations utilizinglocal data resources locally accessible within its corresponding datazone. The data zones in some embodiments are associated with respectiveones of the beacon entities.

These and other illustrative embodiments include, without limitation,methods, apparatus, systems, and processor-readable storage media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an information processing system comprisinga multi-cluster distributed data processing platform in an illustrativeembodiment of the invention.

FIG. 2 is a flow diagram of an exemplary process implemented in themulti-cluster distributed data processing platform of FIG. 1.

FIGS. 3A and 3B show relationships between WWH nodes and associatedHadoop YARN clusters in another illustrative embodiment. These twofigures are collectively referred to herein as FIG. 3.

FIG. 4 compares a YARN application running on a single cluster with adistributed WWH application running on multiple clusters in anillustrative embodiment.

FIGS. 5 and 6 illustrate example arrangements of WWH platform componentsin respective illustrative embodiments.

FIG. 7 shows a more detailed view of a WWH application master in a givencluster and its interaction with similar components in respectiveadditional clusters.

FIGS. 8 through 11 show example software stack diagrams of multi-clusterdistributed data processing platforms in illustrative embodiments.

FIGS. 12 through 16 illustrate example operating configurations ofmulti-cluster distributed data processing platform components inillustrative embodiments.

FIG. 17 shows one possible configuration of a WWH catalog of amulti-cluster distributed data processing platform in an illustrativeembodiment.

FIG. 18 illustrates a method of utilizing a WWH catalog of amulti-cluster distributed data processing platform in an illustrativeembodiment.

FIGS. 19 through 24 illustrate example WWH catalog related features andfunctionality of illustrative embodiments.

FIGS. 25 through 28 show illustrative embodiments of beacon-baseddistributed data processing platforms utilizing WWH functionality.

DETAILED DESCRIPTION

Illustrative embodiments of the present invention will be describedherein with reference to exemplary information processing systems andassociated computers, servers, storage devices and other processingdevices. It is to be appreciated, however, that embodiments of theinvention are not restricted to use with the particular illustrativesystem and device configurations shown. Accordingly, the term“information processing system” as used herein is intended to be broadlyconstrued, so as to encompass, for example, processing systemscomprising cloud computing and storage systems, as well as other typesof processing systems comprising various combinations of physical andvirtual processing resources. An information processing system maytherefore comprise, for example, a plurality of data centers eachcomprising one or more clouds hosting multiple tenants that share cloudresources.

FIG. 1 shows an information processing system 100 comprising amulti-cluster distributed data processing platform in an illustrativeembodiment. The system 100 comprises a plurality of processing nodes102, individually denoted as 102-1, . . . 102-n, . . . 102-N, each ofwhich communicates with one or more Apache Hadoop YARN (“Yet AnotherResource Negotiator”) clusters, individually denoted as 104-1, 104-2, .. . 104-m, . . . 104-M. The processing nodes 102 are configured tocommunicate with one another and with their associated YARN clusters 104over one or more networks that are not explicitly shown in the figure.Apache Hadoop YARN is also referred to as Hadoop 2.0, and is describedin, for example, V. K. Vavilapalli et al., “Apache Hadoop YARN: YetAnother Resource Negotiator,” Proceedings of the 4th Annual Symposium onCloud Computing, SOCC '13, pp. 5:1-5:16, ACM, New York, N.Y., USA, 2013,which is incorporated by reference herein.

The processing nodes 102 are illustratively implemented as respectiveworldwide data nodes, and more particularly as respective worldwideHadoop (WWH) nodes, although numerous alternative processing node typescan be used in other embodiments. The WWH nodes are assumed to beconfigured to perform operations in accordance with any frameworksupported by Hadoop YARN clusters comprising respective ones of the YARNclusters 104. Examples of frameworks supported by each of the HadoopYARN clusters include MapReduce, Spark, Hive, MPI and numerous others.The acronym WWH as used herein is additionally or alternatively intendedto refer to a “worldwide herd” arrangement where the term “herd” in thiscontext illustratively connotes multiple geographically-distributedHadoop platforms. More generally, WWH is used to denote a worldwide dataprocessing platform potentially comprising multiple clusters.

In the FIG. 1 embodiment, the multi-cluster distributed data processingplatform more particularly comprises a WWH platform having one or morelayers of WWH nodes 102 and a plurality of potentiallygeographically-distributed YARN clusters 104. Each of the YARN clusters104 comprises a corresponding cluster of distributed data processingnodes. The WWH platform is illustratively configured for worldwidescale, geographically-dispersed computations and other types ofcluster-based processing based on locally-accessible data resources, aswill be described in more detail elsewhere herein.

It is to be appreciated that a wide variety of other types of processingnodes 102 can be used in other embodiments. Accordingly, the use of WWHnodes in the FIG. 1 embodiment and other embodiments disclosed herein isby way of illustrative example only, and should not be construed aslimiting in any way.

It should also be noted that one or more of the WWH nodes 102 in someembodiments can be part of a corresponding one of the YARN clusters 104.For example, in some embodiments of a WWH platform as disclosed herein,the YARN clusters 104 themselves each comprise one or more layers of WWHnodes. Accordingly, these and other embodiments need not include aseparate layer of WWH nodes 102 above the YARN clusters 104. The WWHnodes 102 may be viewed as examples of what are more generally referredto herein as distributed data processing nodes. The YARN clusters 104are each also assumed to comprise a plurality of additional oralternative distributed data processing nodes.

Each YARN cluster 104 includes a resource manager for that cluster, andfrom a larger perspective YARN can be viewed as a cluster-wide operatingsystem that allows applications to utilize the dynamic and parallelresource infrastructure a computer cluster offers. However, conventionalYARN implementations are generally configured to operate insingle-cluster environments, and do not provide any support for managingdistributed applications which span across more than one cluster.

The WWH platform in the FIG. 1 embodiment is an example of what is moregenerally referred to herein as a “multi-cluster distributed dataprocessing platform.” This WWH platform and other WWH platformsdisclosed herein advantageously extends YARN to multi-clusterenvironments. For example, the WWH platform in some embodiments isconfigured to orchestrate the execution of distributed WWH applicationson a worldwide scale, across multiple, potentiallygeographically-distributed YARN clusters. The WWH platform thereforeprovides a platform for running distributed applications across multipledata zones each having a corresponding YARN cluster.

Other types of multi-cluster distributed data processing platforms maybe implemented in other embodiments. Accordingly, references herein to aWWH platform, YARN clusters and associated features are intended asillustrative examples only, and should not be construed as limiting inany way. For example, other embodiments can be implemented without usingWWH nodes or YARN clusters. Accordingly, it should be understood thatthe distributed data processing techniques disclosed herein are moregenerally applicable to a wide variety of other types of multi-clusterplatforms.

Each of the YARN clusters 104 in the system 100 is associated with acorresponding set of local data resources 110, individually denoted aslocal data resources sets 110-1, 110-2, . . . 110-m, . . . 110-M. Thelocal data resource sets each provide one or more local data resourcesto the corresponding YARN cluster for analytics processing. Results ofthe processing performed within a given YARN cluster utilizing one ormore locally available data resources accessible to that YARN clusterare illustratively provided to one or more other ones of the YARNclusters or to an associated one of the WWH nodes 102 for additionalprocessing associated with provision of analytics functionality withinthe system 100.

The data resources of each of the sets 110 of data resources areindividually identified using the letter R in FIG. 1. Although thesedata resources are illustratively shown as being external to the YARNclusters 104, this is by way of example only and it is assumed in someembodiments that at least a subset of the data resources of a given set110 are within the corresponding YARN cluster 104. Accordingly, a givenYARN cluster can perform processing operations using a combination ofinternal and external local data resources.

The results of the analytics processing performed by a given one of theYARN clusters 104 illustratively comprise results of local analyticsprocessing using YARN frameworks such as MapReduce, Spark and numerousothers.

It should be understood that the above-noted analytics results aremerely examples of what are more generally referred to herein as“processing results” of a given cluster. Such results can take differentforms in different embodiments, as will be readily appreciated by thoseskilled in the art. For example, such processing results can compriselocal analytics results that have been processed in a variety ofdifferent ways within a YARN cluster before being provided to one ofmore of the WWH nodes 102 for additional processing. Numerous othertypes of processing results can be used in other embodiments.

The WWH nodes 102 are each coupled to one or more clients 112. By way ofexample, the set of clients 112 may include one or more desktopcomputers, laptop computers, tablet computers, mobile telephones orother types of communication devices or other processing devices in anycombination. The clients are individually denoted in the figure asclients 112-1, 112-2, . . . 112-k, . . . 112-K. The clients 112 maycomprise, for example, respective end users or associated hardwareentities, software entities or other equipment entities. For example, a“client” as the term is broadly used herein can comprise asoftware-implemented entity running on a user device or other processingdevice within the system 100.

The variables N, M and K denote arbitrary values, as embodiments of theinvention can be configured using any desired number of WWH nodes 102,YARN clusters 104 and clients 112. For example, some embodiments mayinclude multiple YARN clusters 104 and multiple clients 112 but only asingle WWH node 102, or multiple WWH nodes 102 corresponding torespective ones of the YARN clusters 104. Numerous alternativearrangements are possible, including embodiments in which a singlesystem element combines functionality of at least a portion of a WWHnode and functionality of at least a portion of a YARN cluster. Thus,alternative embodiments in which the functions of a WWH node and a YARNcluster are at least partially combined into a common processing entityare possible.

The WWH nodes 102 in some embodiments are implemented at least in partas respective analysis nodes. The analysis nodes may comprise respectivecomputers in a cluster of computers associated with a supercomputer orother high performance computing (HPC) system. The term “processingnode” as used herein is intended to be broadly construed, and such nodesin some embodiments may comprise respective compute nodes in addition toor in place of providing analysis node functionality.

The system 100 may include additional nodes that are not explicitlyshown in the figure. For example, the system 100 may comprise one ormore name nodes. Such name nodes may comprise respective name nodes of aHadoop Distributed File System (HDFS), although other types of namenodes can be used in other embodiments. Particular objects or otherstored data of a storage platform can be made accessible to one or moreof the WWH nodes 102 via a corresponding name node. For example, suchname nodes can be utilized to allow the WWH nodes 102 to addressmultiple HDFS namespaces within the system 100.

Each of the WWH nodes 102 and YARN clusters 104 is assumed to compriseone or more databases for storing analytics processing results andpossibly additional or alternative types of data.

Databases associated with the WWH nodes 102 or the YARN clusters 104 andpossibly other elements of the system 100 can be implemented using oneor more storage platforms. For example, a given storage platform cancomprise any of a variety of different types of storage includingnetwork-attached storage (NAS), storage area networks (SANs),direct-attached storage (DAS), distributed DAS and software-definedstorage (SDS), as well as combinations of these and other storage types.

A given storage platform may comprise storage arrays such as VNXx andSymmetrix VIVIAX® storage arrays, both commercially available from EMCCorporation. Other types of storage products that can be used inimplementing a given storage platform in an illustrative embodimentinclude software-defined storage products such as ScaleIO™ and ViPR®,server-based flash storage devices such as DSSD™, cloud storage productssuch as Elastic Cloud Storage (ECS), object-based storage products suchas Atmos, scale-out all-flash storage arrays such as XtremIO™, andscale-out NAS clusters comprising Isilon® platform nodes and associatedaccelerators in the S-Series, X-Series and NL-Series product lines, allfrom EMC Corporation. Combinations of multiple ones of these and otherstorage products can also be used in implementing a given storageplatform in an illustrative embodiment.

Additionally or alternatively, a given storage platform can implementmultiple storage tiers. For example, a storage platform can comprise a 2TIERS™ storage system from EMC Corporation.

These and other storage platforms can be part of what is more generallyreferred to herein as a processing platform comprising one or moreprocessing devices each comprising a processor coupled to a memory.

A given processing device may be implemented at least in part utilizingone or more virtual machines or other types of virtualizationinfrastructure such as Docker containers or other types of Linuxcontainers (LXCs). The WWH nodes 102 and YARN clusters 104, as well asother system components, may be implemented at least in part usingprocessing devices of such processing platforms.

Communications between the various elements of system 100 may take placeover one or more networks. These networks can illustratively include,for example, a global computer network such as the Internet, a wide areanetwork (WAN), a local area network (LAN), a satellite network, atelephone or cable network, a cellular network, a wireless networkimplemented using a wireless protocol such as WiFi or WiMAX, or variousportions or combinations of these and other types of communicationnetworks.

As a more particular example, some embodiments may utilize one or morehigh-speed local networks in which associated processing devicescommunicate with one another utilizing Peripheral Component Interconnectexpress (PCIe) cards of those devices, and networking protocols such asInfiniBand, Gigabit Ethernet or Fibre Channel. Numerous alternativenetworking arrangements are possible in a given embodiment, as will beappreciated by those skilled in the art.

It is to be appreciated that the particular arrangement of systemelements shown in FIG. 1 is for purposes of illustration only, and thatother arrangements of additional or alternative elements can be used inother embodiments. For example, numerous alternative systemconfigurations can be used to implement multi-cluster distributed dataprocessing functionality as disclosed herein.

Additional details regarding example processing functionality that maybe incorporated in at least a subset of the WWH nodes in illustrativeembodiments are described in U.S. Pat. No. 9,020,802, entitled“Worldwide Distributed Architecture Model and Management,” and U.S. Pat.No. 9,158,843, entitled “Addressing Mechanism for Data at World WideScale,” which are commonly assigned herewith and incorporated byreference herein.

The operation of the system 100 will now be described in further detailwith reference to the flow diagram of FIG. 2. The process as shownincludes steps 200 through 204, and is suitable for use in the system100 but is more generally applicable to other types of multi-clusterdistributed data processing platforms.

In step 200, a first application is initiated in one of a plurality ofdistributed processing node clusters associated with respective datazones, with each of the clusters being configured to perform processingoperations utilizing local data resources locally accessible within itscorresponding data zone. With reference to the FIG. 1 embodiment, afirst application is initiated in one of the YARN clusters 104, possiblyvia one of the WWH nodes 102, by a given one of the clients 112. Thefirst application is illustratively what is referred to herein as a WWHapplication, which is a distributed application for which processing isorchestrated over multiple ones of the YARN clusters 104.

In step 202, a plurality of data resources to be utilized by theapplication are determined. These data resources in the context of theFIG. 1 embodiment illustratively comprise data resources from multipleones of the data resource sets 110.

In step 204, for one or more of the plurality of data resources that areidentified as local data resources, processing operations are performedutilizing the local data resources in the associated cluster inaccordance with the first application. Assuming by way of example thatthe first application in the FIG. 1 embodiment is initiated in the firstYARN cluster 104-1, the data resources identified as local dataresources would include one or more of the data resources from the set110-1.

In step 206, for one or more of the plurality of data resources that areidentified as remote data resources, respective additional applicationsare initiated in one or more additional ones of the plurality ofdistributed processing node clusters. By way of example, if the firstapplication initiated in cluster 104-1 requires processing operationsutilizing remote data resources, such as local data resources of anothercluster 104-2, an additional application is initiated in cluster 104-2so that the processing operations can be performed utilizing the localdata resources available to cluster 104-2.

The identification of the local or remote status of particular dataresources in steps 204 and 206 illustratively involves accessing adistributed catalog service to identify for each of the plurality ofdata resources to be utilized by the application whether the dataresource is a local data resource or a remote data resource. Thedistributed catalog service is illustratively distributed over theclusters with each of the clusters having visibility of a correspondingdistinct portion of the distributed catalog based on its locallyaccessible data resources. In some embodiments, the distributed catalogservice comprises a distributed WWH catalog having a correspondinginstance implemented within each of the clusters. Additional detailsregarding such a WWH catalog and other types of distributed catalogservices that may be used in illustrative embodiments will be providedelsewhere herein.

In step 208, steps 202, 204 and 206 are repeated recursively for eachadditional application that is initiated from the first applicationuntil all processing required by the first application is complete.

For example, assume again with reference to the FIG. 1 embodiment thatone of the clients 112 initiates the first application as a first YARNapplication in the first YARN cluster 104-1. The first cluster 104-1 canthen initiate the one or more additional applications in the one or moreadditional clusters 104-2 through 104-M as respective YARN applicationsfor which the first cluster 104-1 serves as a client such that the oneor more additional clusters are unaware that the one or more additionalapplications are part of a multi-cluster distributed application.

Moreover, at least one of the additional clusters may then determine anadditional plurality of data resources to be utilized by thecorresponding additional application and identify for each of theplurality of additional data resources to be utilized by the additionalapplication whether the data resource is a local data resource that islocally accessible within the data zone of the additional cluster or aremote data resource that is not locally accessible within the data zoneof the additional cluster.

If the additional plurality of data resources includes one or moreremote data resources not locally accessible to the additional cluster,the additional cluster initiates one or more other applications in oneor more other ones of the clusters that have local access to the one ormore remote data resources.

Accordingly, processing operations are performed utilizing the dataresources in the corresponding one or more additional clusters inaccordance with the one or more additional applications. Each remotedata resource identified in a given iteration of step 206 is actually alocal data resource in the particular cluster in which the correspondingprocessing operations are eventually performed. In this embodiment, “allprocessing” is intended to be broadly construed so as to encompass allcluster-based computations to be performed within the clusters utilizingtheir respective sets of local data resources.

In step 210, processing results from the first and one or moreadditional clusters are aggregated and the aggregated processing resultsare provided to the client that submitted the first application.

The aggregation may be performed in some embodiments by the cluster onwhich the first application was initiated, which is illustratively YARNcluster 104-1 in the particular example described above. Alternatively,in other embodiments, aggregation can occur incrementally on multipleones of the clusters.

The processing results from the first and one or more additionalclusters advantageously preserve privacy of those clusters in theirrespective local data resources. For example, the processing resultsfrom a given one of the clusters may be permitted to be transmitted toanother one of the clusters but the corresponding local data resourcesof the given cluster that are utilized to obtain the transmittedprocessing results are not permitted to be transmitted to another one ofthe clusters.

Similar advantages are provided with regard to other aspects of dataprotection, including data security.

The particular processing operations and other system functionalitydescribed in conjunction with the flow diagram of FIG. 2 are presentedby way of illustrative example only, and should not be construed aslimiting the scope of the invention in any way. Alternative embodimentscan use other types of processing operations for implementingmulti-cluster distributed data processing functionality. For example,the ordering of the process steps may be varied in other embodiments, orcertain steps may be performed concurrently with one another rather thanserially. Also, one or more of the process steps may be repeatedperiodically for different types of analytics functionality, or multipleinstances of the process can be performed in parallel with one anotheron different WWH platforms or other types of platforms implementedwithin a given information processing system.

It is to be appreciated that functionality such as that described inconjunction with the flow diagram of FIG. 2 can be implemented at leastin part in the form of one or more software programs stored in memoryand executed by a processor of a processing device such as a computer orserver. As will be described below, a memory or other storage devicehaving executable program code of one or more software programs embodiedtherein is an example of what is more generally referred to herein as a“processor-readable storage medium.”

Illustrative embodiments can provide a number of significant advantagesrelative to conventional arrangements.

For example, some embodiments provide WWH platforms that are faster andmore efficient than conventional analytics systems. Moreover,multi-cluster distributed data processing platforms in some embodimentsare implemented in a decentralized and privacy-preserving manner. Theseand other multi-cluster distributed data processing platformsadvantageously overcome disadvantages of conventional practice, which asindicated previously often rely on copying of local data to acentralized site for analysis, leading to privacy and performanceconcerns.

In some embodiments, a multi-cluster distributed data processingplatform is configured to leverage Big Data profiles and associated BigData analytics in processing local and remote data resources acrossmultiple geographic regions or other types of data zones.

Additional details regarding Big Data profiles and associated Big Dataanalytics that can be implemented in illustrative embodiments of thepresent invention are described in U.S. Pat. No. 9,031,992, entitled“Analyzing Big Data,” which is commonly assigned herewith andincorporated by reference herein.

A multi-cluster distributed data processing platform in an illustrativeembodiment can utilize the data scattered across multiple regional datacenters located worldwide, while preserving data privacy and adjustingfor differences in data formats and other factors between the variousdata centers.

A WWH platform in some embodiments leverages one or more frameworkssupported by Hadoop YARN, such as MapReduce, Spark, Hive, MPI andnumerous others, to support distributed computations while alsominimizing data movement, adhering to bandwidth constraints in terms ofspeed, capacity and cost, and satisfying security policies as well aspolicies relating to governance, risk management and compliance.

FIGS. 3A and 3B illustrate another information processing system 300comprising a WWH platform. The WWH platform in this embodiment comprisesa WWH node layer 301 that includes multiple WWH nodes 302 such as WWHnodes 302-1 and 302-2. The WWH platform further comprises a YARN clusterlayer 303 that includes multiple YARN clusters 304 such as YARN cluster304-1 and YARN cluster 304-2. The WWH nodes 302 are associated withrespective ones of the YARN clusters 304.

The YARN clusters 304 are examples of what are more generally referredto herein as “distributed processing node clusters.” Thus, like the YARNclusters 104 of the FIG. 1 embodiment, each of the YARN clusters 304 isassumed to include a cluster of multiple computers or other processingdevices. Other types of distributed processing node clusters can be usedin other embodiments. The use of Hadoop YARN in the FIG. 3 embodiment isby way of example only, and other embodiments need not utilize HadoopYARN.

Also, although single layers 301 and 303 of respective sets of WWH nodes302 and YARN clusters 304 are shown in this figure, other embodimentscan include multiple layers of WWH nodes, multiple layers of YARNclusters, or both multiple layers of WWH nodes and multiple layers ofYARN clusters.

In the information processing system 300, there is a one-to-onecorrespondence between the WWH nodes 302 and the respective YARNclusters 304, although this is also by way of illustrative example only.In other embodiments, a given WWH node may be associated with multipleYARN clusters. Additionally or alternatively, a given YARN cluster canbe associated with multiple WWH nodes.

It is also possible that one or more of the WWH nodes 302 may eachcomprise a data processing node of the corresponding YARN cluster 304.Thus, in some embodiments, the separate layers 301 and 303 of the FIG. 3embodiment are merged into a single layer of YARN clusters one or moreof which each include one or more WWH nodes. Such an arrangement isconsidered yet another illustrative example of a WWH platform, or moregenerally a multi-cluster distributed data processing platform, as thoseterms are broadly utilized herein.

The YARN clusters 304 in the FIG. 3 embodiment are assumed to beassociated with respective distinct data zones. Each of the YARNclusters 304 is configured to perform processing operations utilizinglocal data resources locally accessible within its corresponding datazone. The YARN clusters as illustrated in the figure illustrativelycomprise respective processing platforms including various arrangementsof multi-node clouds, virtual infrastructure components such as virtualmachines (VMs) and virtual networks, Isilon® platform nodes, and otherexample arrangements of distributed processing nodes.

By way of example, at least a subset of the YARN clusters 304 maycomprise respective geographically-distributed regional data centerseach configured to perform analytics processing utilizing the locallyaccessible data resources of its corresponding data zone. Additional oralternative types of boundaries may be used to separate the system 300into multiple data zones. Accordingly, geographical distribution of thedata zones and their respective clusters is not required.

The WWH nodes 302 illustratively utilize processing results from one ormore of the YARN clusters 304 in orchestrating distributed applicationsover multiple YARN clusters in the system 300. This is achieved in amanner that preserves the privacy of those clusters in their respectivelocal data resources. For example, processing results from a given oneof the clusters may be permitted to be transmitted to another one of theclusters while the local data resources of the given cluster that areutilized to obtain the processing results are not permitted to betransmitted to another one of the clusters.

As illustrated in FIG. 3A, the WWH layer 301 may be viewed as comprisingan “analytics layer” of the system. The YARN clusters 304 can beinterconnected in different ways at that layer through use of differentconnections between the WWH nodes 302. In this particular figure, afirst WWH node 302-1 is shown as being interconnected with each of theother WWH nodes 302 of the WWH layer 301.

FIG. 3B illustrates that alternative interconnections of the WWH nodes302 are possible, including the arrangement shown in which another WWHnode 302-2 initiates connections with each of the other WWH nodes 302 inorchestrating a given distributed application over multiple ones of theYARN clusters 304. It is to be appreciated that, in the FIG. 3embodiment, any of the WWH nodes 302 can initiate a distributedapplication on its corresponding one of the YARN clusters 304 and thatdistributed application can subsequently initiate multiple additionalapplications involving respective additional ones of the clusters.

Again, the particular arrangements of layers, nodes and clusters shownin FIG. 3 are presented by way of example only, and should not beconstrued as limiting in any way.

The WWH platform in the FIG. 3 embodiment and one or more otherembodiments disclosed herein illustratively adheres to local processingwithin each cluster using data locally accessible to that cluster. Thisis achieved without the need for implementing a distributed file systemover the multiple clusters. Also, movement of data resources betweenclusters is avoided. Instead, data resources are processed locallywithin their respective YARN clusters.

This orchestration of distributed applications over multiple YARNclusters is facilitated in illustrative embodiments through the use ofwhat is referred to herein as a WWH catalog. The WWH catalog is acatalog of data resources, and is an example of what is more generallyreferred to herein as a “distributed catalog service.”

In some embodiments, each cluster that is part of the WWH platform hasaccess to or otherwise comprises an instance of the WWH catalogimplemented for that cluster. The WWH catalog instance implemented for agiven cluster illustratively contains detailed information regardinglocal data resources of that cluster, such as, for example, file namesand metadata about the files and their content, and references to one ormore other clusters in the case of a non-local resource. This creates ahierarchical structure to execution of a WWH application within the WWHplatform.

It should be noted that each YARN cluster need not include its owninstance of the WWH catalog. For example, in some embodiments, only asubset of the YARN clusters of a multi-cluster distributed dataprocessing platform implement respective instances of a distributed WWHcatalog. In such an arrangement, YARN clusters that do not includerespective WWH catalog instances can nonetheless participate inperformance of computations associated with a distributed WWHapplication.

A WWH application identifies data files and other input data items fromamong the various data resources characterized by the WWH catalog. Agiven such input data item can more particularly comprise, for example,a text file, an XML file, a result relation of a database query or aresult of an API query.

Data resources characterized by the WWH catalog can be considered globalin the sense that clients are oblivious to the particular location ofthe resource. For example, a given resource can be comprised of severalother resources, each residing in a different data zone. A meta-resourceis a piece of data that describes a corresponding data resource. Itgenerally includes the location of the resource and information abouthow to access the resource.

The WWH catalog is distributed over the clusters of the WWH platformwith each of the clusters having visibility of only its correspondinginstance of the WWH catalog. In some embodiments, the distributedinstances of the WWH catalog are implemented as respective YARNapplications running on respective ones of the YARN clusters of the WWHplatform.

A given instance of the WWH catalog on a corresponding one of the YARNclusters typically comprises a plurality of entries with each such entrycomprising a meta-resource including information characterizing locationand accessibility of a corresponding one of the data resources. By wayof example, the meta-resource for a given local data resource maycomprise a file path to a storage location of that local data resourcein the corresponding YARN cluster. Also by way of example, themeta-resource for a given remote data resource may comprise informationidentifying another cluster for which that data resource is a local dataresource.

A given meta-resource of the WWH catalog may additionally oralternatively comprise one or more other types of information, such as,for example, information regarding transformation of the data resourceinto one or more designated formats, access control information, policyrules, etc.

The WWH catalog therefore illustratively provides a catalog of entries,each comprising a meta-resource. Each meta-resource describes therespective resource and may contain the code or an API required totransform the resource to the format required by the application. Endusers or other types of clients may browse the WWH catalog via abrowsing API or other type of browsing interface in order to obtaininformation about meta-resources, and WWH applications may query it forinformation about how to access the data. As noted above, the WWHcatalog is assumed to be distributed across multiple data zones andtheir respective YARN clusters. Such a distributed arrangement helps toprovide security and privacy for the underlying data resources.

Although distributed implementations of the WWH catalog are advantageousin some embodiments, it is possible in other embodiments for the WWHcatalog to be implemented in only a single cluster of a WWH platform.Other alternative implementations may include distributedimplementations in which the WWH catalog is distributed over only asubset of the clusters of a WWH platform, rather than over all of theclusters of the WWH platform.

The WWH platform and its associated WWH catalog in illustrativeembodiments implement a recursiveness property that allows a givendistributed application initiated on one of the YARN clusters toinitiate additional applications on respective additional ones of theYARN clusters. Those additional applications can similarly initiate moreapplications on other ones of the YARN clusters different than the YARNclusters on which the additional applications were initiated. In thismanner, a distributed application can be executed utilizing local dataresources of multiple YARN clusters while preserving the privacy of eachof the YARN clusters in its local data resources.

In some embodiments, security measures are deployed that prevent thedata zones from being accessible to the outside world. For example,firewalls, routers and gateways may prevent public access to a clusterof a given data zone, allowing access to the cluster only from within acertain access point. The WWH platform in illustrative embodiments isconfigured to allow such “hidden” data zones to take part in bothsharing data and computation.

The execution of a WWH application can be represented in someembodiments as a tree or a directed graph. In such an arrangement, eachdata zone participating in the execution of the application may beviewed as having two roles: (1) it receives a request to execute anapplication from a client, and (2) it can send requests for execution toother data zones, acting like a client. Role (1) can be represented as a“parent” node in the graph, and role (2) can be represented as an edgefrom a parent node to one or more “child” nodes. Each data zone maytherefore be represented as the parent node of one or more child nodes,but may also be represented as the child node of another parent noderepresentative of another data zone. A given parent node may not haveaccess to data resources needed by a given application, but one or moreof its associated child nodes may have access to those resources. Thestructure of the tree or directed graph representative of a given WWHapplication can be made visible with appropriate permissions via thedistributed WWH catalog.

A WWH platform configured to run applications across multiple clustersassociated with respective distinct data zones is advantageous in termsof both privacy and performance. Privacy is provided in that anapplication submitted to an initial cluster corresponding to a specificdata zone accesses the data local to that data zone. The results of theapplication execution in the initial cluster may be transferred to otherclusters corresponding to respective other data zones, but suchprocessing results are typically aggregated and therefore need notinclude any private information. Furthermore, the recursiveness propertymentioned above can in some embodiments be configured so as to hide eventhe knowledge of which of the clusters participate in the applicationexecution. For similar reasons, performance is greatly improved. Usuallyraw data stays in its original location and only the results which areof much smaller size may be transferred between clusters. Thiscontributes to improved performance both because of the inherentparallelism and the reduced data transfer between clusters.

As is apparent from the above, the overall privacy and efficiency of theWWH platform is maintained in some embodiments by adhering to localprocessing within clusters and their associated data zones. In order tokeep the processing local, the WWH catalog includes meta-resources thatdirect the computation to the cluster where the data is stored, suchthat the computation moves and the data does not.

The WWH platform in illustrative embodiments provides significantadvantages relative to conventional systems. For example, the WWHplatform in some embodiments is oblivious to the particular local filesystems utilized in the respective YARN clusters. Moreover, the WWHplatform keeps local raw data private within each of the clusters, doesnot need a centralized controller or scheduler, and is not limited touse with only the MapReduce framework but is more generally suitable foruse with any of a wide variety of frameworks that are supported by YARN.

The WWH platform utilizes a distributed WWH catalog having instancesaccessible to respective ones of the YARN clusters, and is thus agnosticto where exactly the data resides, and its exact format, and does notrequire a global file system.

The WWH platform is strongly privacy aware. It supports and encourageslocal processing of local data and provides simple ways for sendingintermediate processing results which do not contain private informationbetween clusters.

The WWH platform provides similar advantages for other aspects ofGovernance, Risk and Compliance (GRC). For example, by pushingprocessing closer to where the data is located, the WWH platformfacilitates enforcement of policies relating to governance, managementof risk, and compliance with regulatory requirements, all at the locallevel.

The WWH platform supports multiple data zones. A data zone isillustratively a distinct YARN cluster with its own local data. Such adata zone will usually execute a YARN application such as a MapReduceapplication on its local data. The WWH platform provides a frameworkwhich spans across multiple data zones, and enables the combination ofprocessing results based on local data resources of the respective datazones in a global manner. Thus, the WWH platform provides and encouragescooperation between different data zones. However, the WWH platform doesnot encourage moving raw data between data zones, for both performanceand privacy reasons, as well as for other related reasons such as theabove-noted facilitation of GRC at the local level.

The WWH platform in some embodiments has an open architecture in thesense that any YARN cluster can join the WWH platform, and therefore theWWH platform in such an embodiment does not require any singlecentralized controller. Every participating YARN cluster is in controlof the data it wishes to share with the outside world. An authorizedexternal client can connect to any data zone supported by the WWHplatform and there is no single entry point.

The WWH platform can be illustratively implemented utilizing YARNapplications. For example, when a client wishes to run a WWH applicationit contacts a first one of the clusters, and runs a YARN application onthat cluster. When other clusters need to be contacted, one or morecontainers of the first cluster act like respective clients for theother clusters, and run YARN applications on those other clusters. Thusin each individual cluster the distributed WWH application is seen as anindividual YARN application and YARN itself is not aware of the multipledata zone aspects of the WWH application or the WWH platform.

Like YARN itself, the WWH platform in some embodiments is functionallyseparated into a platform layer and a framework layer. The WWH frameworklayer can be configured to support WWH frameworks for executing WWHapplications that utilize any of a wide variety of underlying YARNframeworks. A developer can write WWH frameworks, and clients will beable to use those WWH frameworks, in a manner similar to how YARNframeworks such as MapReduce or Spark are utilized on single clusters.For example, some embodiments of WWH platforms described herein areprovided with a WWH framework for running MapReduce applications indifferent data zones associated with respective multiple YARN clustersand using a global reducer in a particular YARN cluster to compute thefinal results. Alternatively, the global reducer can be implemented atleast in part outside of the YARN clusters, such as within a given oneof the WWH nodes.

Additional details regarding illustrative embodiments of a WWH platformwill now be described with reference to FIGS. 4 through 7.

In these embodiments, it is assumed that a WWH application comprisesexecutable code that is configured to process a set oflocation-dependent data resources using a set of distributed servicesprovided by the WWH platform. The location-dependent data resources caninclude Big Data or other types of data subject to processing usingdistributed analytics applications.

Like YARN applications utilizing frameworks such as MapReduce and Spark,WWH applications can utilize corresponding WWH frameworks denoted hereinas WWH-MapReduce and WWH-Spark. The WWH applications illustrativelyinclude client applications that utilize these and other WWH frameworks.Any framework supported by YARN can have a corresponding WWH frameworkimplemented using the techniques disclosed herein.

Software developers associated with the WWH platform illustrativelyinclude the above-noted clients that create applications which benefitfrom the distributive nature of the WWH platform using the WWHframeworks. For example, such a client may comprise a developer thatwrites an application comprising Mapper, Reducer and GlobalReducercomponents and then submits a job using a WWH-MapReduce-GlobalReduceframework.

Other developers include platform developers that write the componentswhich are considered integral parts of the WWH platform, and frameworkdevelopers that develop the WWH frameworks to be utilized by clients increating their applications. Examples of WWH frameworks include theabove-noted WWH-MapReduce, WWH-Spark and WWH-MapReduce-GlobalReduceframeworks.

Referring now to FIG. 4, a YARN application running on a single clusterdenoted Cluster 0 is compared to a WWH application running on multipleclusters including Cluster 0 and two additional clusters denoted Cluster1 and Cluster 2.

As illustrated in the figure, the YARN application comprises anapplication master that controls the execution of a correspondingapplication using multiple containers in the same cluster. The WWHapplication comprises multiple application masters running on respectiveones of Cluster 0, Cluster 1 and Cluster 2. Each of the applicationmasters of the WWH application is associated with an application runningin the corresponding cluster and includes a corresponding WWHaggregator. Each of the WWH aggregators is controlled by its applicationmaster and utilizes multiple containers within its cluster inconjunction with execution of the associated application.

A given container illustratively comprises a collection of physicalresources on a single data processing node, such as memory (e.g., RAM),CPU cores, and disks. There can be multiple containers on a single node,or a single large container on that node. Each node of a given clusteris assumed to comprise one or more containers of a designated minimummemory size (e.g., 512 MB or 1 GB). The application master can requestone or more containers as a multiple of the minimum memory size.

The multiple containers utilized by one of the WWH aggregators on agiven one of the clusters correspond to respective local data resourcesthat are locally accessible within that cluster. The WWH aggregator isillustratively configured to request initiation of one or moreadditional applications on one or more respective other ones of theclusters with the additional application utilizing remote data resourceslocally accessible within the one or more other clusters. The WWHapplication master component corresponding to the WWH aggregator may beconfigured to access a resolving API or other type of resolvinginterface of the distributed WWH catalog instance of the correspondingcluster in order to determine for each of the plurality of dataresources to be utilized by the application whether the data resource isa local data resource or a remote data resource.

Although each WWH application master in this embodiment is shown asinteracting with only a single WWH aggregator, this is by way ofillustrative example only and in other embodiments a given WWHapplication master can be configured to control multiple WWHaggregators.

Also, the particular separation between WWH application master and WWHaggregator components is exemplary only, and in other embodiments agiven WWH aggregator or its associated functionality can be incorporatedat least in part within the corresponding WWH application master ratherthan external to that WWH application master as illustrated in FIG. 4and other embodiments herein.

The WWH application masters are also referred to herein as respectiveWWH-ApplicationMaster (“WAM”) components. Such components are assumed tocomprise WWH platform components that are “private” and therefore notmodifiable by framework developers. These private components are assumedto be defined and implemented by the platform developers.

Other WWH platform components considered private in illustrativeembodiments include WWH Node Manager and WWH Catalog Service. These andother WWH platform components will be described in greater detail below.

The WWH aggregators are also referred to herein as WWH-Aggregatorcomponents. Such components are assumed to comprise WWH platformcomponents that are “exposed” and therefore are modifiable by frameworkdevelopers. For example, a framework developer can create an extensionto an abstract WWH-Aggregator class. An example of such an extension fora WWH-MapReduce framework is denoted herein asWWH-Aggregator-For-MapReduce. The role of the WWH-Aggregator isgenerally to aggregate processing results from multiple clusters and topresent the aggregated processing results to an end user or other clientthat initiated the distributed application.

It should be noted that references herein to private and exposed WWHplatform components are made by way of example only, and in otherembodiments additional or alternative components may be in respectiveones of the private and exposed categories. Also, in other embodiments,all or substantially all WWH platform components may be designated asprivate, or all or substantially all WWH platform components may bedesignated as exposed.

A given WWH-Application illustratively comprises a set of executablecomponents, such as binaries, classes and other components, includingthe WWH-ApplicationMaster class and one or more derivatives of theWWH-Aggregator class bundled with associated arguments for a ResourceManager of the corresponding YARN cluster in which the WWH-Applicationis initiated. These components collectively permit initiation of thecorresponding distributed application.

A given WWH-Aggregator may utilize the containers, files and other dataresources that are local to the particular cluster on which it runs. Inaddition, the given WWH-Aggregator may recursively request the executionof a remote WWH-Aggregator in a remote cluster. This may be achieved atleast in part utilizing a Representational State Transfer (REST)application programming interface (API) of the correspondingWWH-ApplicationMaster.

As noted above, client applications can be configured to utilize one ofa plurality of available WWH frameworks, such as one of theWWH-MapReduce, WWH-Spark and WWH-MapReduce-GlobalReduce frameworks. Thelatter WWH framework and a corresponding WWH global MapReduceapplication flow utilizing that framework will be described in greaterdetail below. The global MapReduce application is just one example of adistributed WWH application that can be executed using a WWH platform asdisclosed herein.

FIGS. 5 and 6 illustrate example arrangements of WWH components inrespective illustrative embodiments.

Referring initially to FIG. 5, a portion 500 of a WWH platform is shown.The portion 500 includes only a single YARN cluster 504-1, although itis to be appreciated that the WWH platform is assumed to comprisemultiple additional clusters that are not explicitly shown in thefigure. Clients 512-1 and 512-2 interact with the cluster 504-1. Thecluster 504-1 comprises a plurality of distributed data processing nodeshaving respective node managers (NMs) 520-1, 520-2 and 520-3. Thecluster 504-1 has an associated resource manager (RM) 525. The resourcemanager 525 is assumed to comprise a YARN resource manager. It isresponsible for allocating resources and scheduling of containers withinits corresponding cluster 504-1.

A given one of the node managers 520 manages a corresponding one of thedata processing nodes of the cluster 504-1. This includes keepingup-to-date with the resource manager 525, managing the life-cycle ofapplication containers, monitoring resource usage of individualcontainers, monitoring node health, and managing logs and otherauxiliary services that can be utilized by YARN applications.

On startup, the given node manager registers with the resource manager525, and then sends heartbeats with its status and waits forinstructions. Its primary goal is to manage application containersassigned to it by the resource manager. For each container there is asingle node manager that is responsible for its lifecycle.

In this embodiment, clients 512-1 and 512-2 communicate with respectiveWWH application master (WAM) components running on data processing nodeshaving node managers 520-1 and 520-3. This communication occurs via RESTAPIs of the respective WAM components. The clients 512 and WAMcomponents also communicate with the resource manager 525 via YARNremote procedure calls (RPCs) as illustrated. It should be noted thatthe node managers 520 are responsible for the execution of theapplication processes within their corresponding cluster 504-1.

FIG. 6 shows a portion 600 of a WWH platform in another illustrativeembodiment. In this embodiment, first and second YARN clusters 604-1 and604-2 have associated resource managers 625-1 and 625-2. A client 612-1interacts with a WAM component in cluster 604-1 via a REST API of theWAM component in that cluster. That WAM component interacts with two WWHaggregators also running in the cluster 604-1, and with another WAMcomponent implemented in cluster 604-2. The other WAM componentimplemented in cluster 604-2 interacts with a single WWH aggregator alsorunning in the cluster 604-2. The resource manager 625-1 communicateswith the client 612-1 and the WAM component of cluster 604-1 via YARNRPCs. Similarly, the resource manager 625-2 communicates with the WAMcomponents in respective clusters 604-1 and 604-2 via YARN RPCs.Communications between the WAM components and between a given one of theWAM components and its corresponding WWH aggregator(s) are carried outvia the REST API of the given WAM component.

FIG. 7 shows a more detailed view of a WAM component in a given clusterand its interaction with similar components in respective additionalclusters. In this illustrative embodiment, a portion 700 of a WWHplatform comprises YARN clusters 704-1, 704-2, 704-3 and 704-4. It isassumed that each of the YARN clusters has an associated resourcemanager, although the resource managers are not explicitly shown in thefigure. The YARN cluster 704-1 comprises a WAM component 730-1. Thecluster 704-1 is the local cluster of the WAM component 730-1, and theother clusters 704-2, 704-3 and 704-4 are respective remote clustersrelative to the local cluster 704-1.

The WAM component comprises a REST API 735-1, a WWH cluster node managerfor its local cluster 704-1, and additional WWH cluster node managersfor respective ones of the remote clusters 704-2, 704-3 and 704-4. Eachof the remote clusters 704-2, 704-3 and 704-4 includes a WAM componentthat is assumed to be configured in a manner similar to WAM component730-1 of local cluster 704-1.

A client 712-1 interacts with WAM component 730-1 via the REST API735-1. The WAM component 730-1 communicates with the WWH aggregator ofits local cluster 704-1 via the REST API and the local cluster nodemanager. Also, the WWH aggregator is configured to interact with thelocal and remote cluster node managers. For example, the WWH aggregatorcan communicate with the local and remote cluster node managers of theWAM component 730-1 via the REST API 735-1. Accordingly, in thisembodiment, the REST API 735-1 allows both the client 712-1 and the WWHaggregator of the WAM component 730-1 to communicate with the local andremote cluster node managers.

The WAM component 730-1 is also referred to herein as aWWH-ApplicationMaster, and as previously described is assumed to be aprivate component of the WWH platform that cannot be altered byframework developers. The WWH-ApplicationMaster is a YARNApplicationMaster, and is the main process which provides WWH-relatedservices in this embodiment. It contains the REST API 735-1, whichallows external clients to access the corresponding WWH-Application, andfacilitates job distribution between the different components of theWWH-Application as utilized by the WWH-Aggregator. The local and remotecluster node managers of the WWH-ApplicationMaster collectively comprisea set of WWH-ClusterNodeManager threads that are created on demand andare responsible for the actual distribution and monitoring of jobs forthe local and remote clusters. The WWH-ApplicationMaster is alsoresponsible for communication between clusters. This is achieved in thepresent embodiment by using the remote cluster node managers eachbehaving as a YARN client to a corresponding remote cluster.

A WWH-ClusterNodeManager is also assumed to be a private component ofthe WWH platform. As noted above, the WWH-ClusterNodeManager is a threadinside the WWH-ApplicationMaster. It can be either local or remotedepending on whether it communicates with the resource manager in thesame cluster as the WAM component or with the resource manager in aremote cluster.

A local WWH-ClusterNodeManager is responsible for executing the localapplication via the execution of a supplied WWH-Aggregator and forupdating the WWH-ApplicationMaster REST API so that recursively theparent or invoking WWH-Aggregator will be able to fetch back theprocessing results.

A remote WWH-ClusterNodeManager recursively serves as a client to theremote WWH-ApplicationMaster and passes the jobs through its remote RESTAPI.

The WWH-ClusterNodeManager components are created on demand when a jobis submitted to the WWH-ApplicationMaster. Note that since theWWH-ClusterNodeManager is a YARN client, the communication between theWWH-ClusterNodeManager and the other clusters is in accordance with YARNprotocols.

As mentioned previously, the WWH-Aggregator component is assumed to bean exposed component of the WWH platform, and is therefore subject tomodification by framework developers. The WWH-Aggregator isillustratively implemented as a child container of theWWH-ApplicationMaster. It may use the containers, files and other localdata resources of the cluster it is running in. Additionally oralternatively, it may recursively request execution of a remoteWWH-Aggregator in a remote cluster using the REST API of theWWH-ApplicationMaster. The WWH-Aggregator is responsible for aggregatingthe processing results of submitted jobs and producing a meaningfulresult for the client. Each WWH-Aggregator illustratively has anassociated WWH-ApplicationMaster container that is responsible for thatWWH-Aggregator.

It is to be appreciated that the particular arrangements of WWH platformcomponents illustrated in FIGS. 4 through 7 are presented by way ofillustrative example only. Numerous other arrangements of additional oralternative components can be used to implement a multi-clusterdistributed data processing platform in other embodiments.

Additional examples of software stack arrangements for illustrativeembodiments of multi-cluster distributed data processing platforms areshown in FIGS. 8 through 11.

With reference now to FIG. 8, a given multi-cluster distributed dataprocessing platform can comprise a YARN layer built over an underlyingHDFS. The YARN layer supports YARN frameworks such as MapReduce andSpark, and possibly numerous others. It also supports a WWH frameworkthat itself includes WWH-MapReduce and WWH-Spark frameworks, andpossibly numerous other WWH frameworks.

FIGS. 9, 10 and 11 show various alternative arrangements of softwarecomponents that may be utilized in a software stack of a multi-clusterdistributed data processing platform in other embodiments.

For example, with reference to FIG. 9, a YARN layer supports multipleframeworks including WWH, MapReduce, Spark and MPI, and makes use of anunderlying HDFS. The HDFS can also support other projects, such as, forexample, Hbase. Other projects not involving use of YARN or HDFS canalso be implemented in the platform.

Another example platform software stack is illustrated in FIG. 10. Inthis embodiment, a YARN layer supports multiple frameworks including WWHand MapReduce distributed processing, and makes use of an underlyingHDFS. The MapReduce distributed processing utilizes HCatalog metadataservices to support Hive queries, Pig scripts and other functionality.The HDFS can also support other projects, such as, for example, Hbase.Other projects not involving use of YARN or HDFS can also be implementedin the platform.

With reference now to FIG. 11, a further example of a platform softwarestack is shown. In this embodiment, a YARN layer supports multipleframeworks including WWH distributed processing and MapReducedistributed processing, and makes use of an underlying HDFS. TheMapReduce distributed processing utilizes HCatalog metadata services tosupport Hive queries, Pig scripts and other functionality. The WWHdistributed processing utilizes WWHCatalog metadata services to supportWWH queries and WWH scripts. Again, the HDFS can also support otherprojects, such as, for example, Hbase, and other projects not involvinguse of YARN or HDFS can also be implemented in the platform.

It is to be appreciated that the particular platform software stacksillustrated in FIGS. 8 through 11 are examples only, and numerous othermulti-cluster distributed data processing platforms can be configuredusing respective alternative types and configurations of softwarecomponents.

FIGS. 12 through 16 illustrate example operating configurations ofmulti-cluster distributed data processing platform components inillustrative embodiments. The circled numbers shown in FIGS. 13, 14 and15 are indicative of example processing sequence flows utilized in theseillustrative embodiments.

Referring initially to FIG. 12, example relationships between theportions of a given WWH implementation that are accessible to a WWHclient developer, WWH framework developer and WWH platform developer areshown. In this embodiment, the WWH platform developer implementsWWH-ApplicationMaster, WWH-ClusterNodeManager, andWWH-Aggregator-AbstractClass. The WWH framework developer implementsWWH-AggregatorMapReduce, WWH-AggregatorSpark and WWH-AggregatorMPI. TheWWH client developer implements My-WWH-MapReduceApplication,My-WWH-SparkApplication and My-WWH-MPIApplication.My-WWH-MapReduceApplication is a client-developed application thatutilizes underlying framework and platform components includingWWH-AggregatorMapReduce, WWH-AggregatorAbstractClass,WWH-ApplicationMaster and WWH-ClusterNodeManager, as illustrated.

With reference now to FIG. 13, an embodiment is illustrated in which allof the data resources required by an application submitted by a clientare local resources within the cluster that initiates the application.In this embodiment, a YARN cluster comprises a single resource manager,and multiple node managers corresponding to respective data processingnodes of the YARN cluster.

The client in the FIG. 13 embodiment submits an application using theGlobal Map Reducer framework to Cluster 0 and all the data resourcesactually reside in Cluster 0 itself. First, the client submits anapplication to the Resource Manager residing in Cluster 0 (1), whichcreates an instance of the WWH Application Master (2) and passes to theWWH Application Master all the parameters received by the client,including the mapper, the local reducer, the global reducer, and thelist of resources to be used. The WWH Application Master uses theResolving API to communicate with the WWH Catalog Master, passing thelist of resources to be used (3). Since all the resources are local inthis embodiment, the WWH Catalog Master will return the actual addressof the list of resources to the WWH Application Master. The WWHApplication Master will then create an instance of the WWH Aggregator(4), to manage the collection of results from the WWH Cluster NodeManagers and to execute the Global Reduce operation later on. Next, theWWH Application Master will create an instance of the WWH Cluster NodeManager (5) passing the mapper, the local reducer and the list of localresources. The WWH Cluster Node Manager just created will behave as alocal client to the Resource Manager running in Cluster 0 itself,submitting a request for the execution of a MapReduce operation inCluster 0 (6). The local Resource Manager in Cluster 0 will then createan instance of the Application Master (7). From this point on, theApplication Master just created will behave as a normal YARN application(8). The Application Master will analyze the list of resources and thennegotiate with the scheduler in the local Resource Manager of Cluster 0the allocation of processing resources with the Node Managers.

FIG. 14 illustrates an embodiment in which the data resources requiredby an application submitted by a client are remote data resources inrespective additional YARN clusters other than the YARN cluster thatinitiates the application. In this embodiment, the client submits anapplication in Cluster 0 and the data resources reside in Cluster 1 andCluster 2. More particularly, the client submits an application to theResource Manager residing in Cluster 0 (1), which creates an instance ofthe WWH Application Master (2), which then connects with the WWH CatalogMaster (3) through the Resolving API. In this embodiment, the WWHCatalog Master returns a list of resources containing resources thatreside in Cluster 1 and resources that reside in Cluster 2. The WWHApplication Master then creates an instance of the WWH Aggregator (4)and then creates an instance of the WWH Cluster Node Manager forcommunicating with Cluster 1 (5) and an instance of the WWH Cluster NodeManager for communicating with Cluster 2 (6).

Optimizations have been done in the implementation where there is asingle WWH Cluster Node Manager for communication between a given pairof clusters. In other words, should another application start in Cluster0 that also has resources residing in Cluster 1, the system would notcreate another instance of the WWH Cluster Node Manager in Cluster 0,but would instead actually utilize the same instance already created.The WWH Cluster Node Managers then start an application in the clustersthat they are connected to (5-1 and 6-1, respectively), and become aclient of the Resource Managers in those respective clusters. TheResource Managers in Cluster 1 and Cluster 2 then create a WWHApplication Master in their respective clusters (5-2 and 6-2) which willexecute the application with the data resources in the respectiveclusters.

FIG. 15 illustrates an embodiment in which the data resources requiredby an application submitted by a client include both local resourceswithin the YARN cluster that initiates the application and remote dataresources in respective additional YARN clusters other than the YARNcluster that initiates the application. In this embodiment, the clientsubmits an application request to the Resource Manager residing inCluster 0 (1) that creates a WWH Application Master (2) that thenconnects with the WWH Catalog Master (3). The WWH Catalog Master thenreturns a list of resources residing in Cluster 0, a list of resourcesresiding in Cluster 1, and a list of resources residing in Cluster 2.The WWH Application Master then creates a WWH Aggregator (4) and thencreates a WWH Cluster Node Manager for each one of the clusters that hasresources involved in this computation (5, 6 and 7). The WWH ClusterNode Managers then communicate with the Resource Managers residing inthe respective clusters and submit respective applications to be startedthere (5-1, 6-1 and 7-1). The Resource Manager in Cluster 0 starts anApplication Master (5-2) while the Resource Managers in the remoteclusters start respective WWH Application Masters (6-2 and 7-2).

An example of one possible arrangement of WWH components in anillustrative embodiment is shown in FIG. 16. In this embodiment, a YARNcluster having a resource manager interacts via a client servicesinterface with WWH distributed processing components and WWH catalogmetadata services components. These WWH components are also accessiblevia RESTful API services as indicated.

Various features of possible configurations of the WWH catalog areillustrated in FIGS. 17 and 18.

Referring initially to FIG. 17, a portion 1700 of a multi-clusterdistributed data processing platform in an illustrative embodimentcomprises a first YARN cluster 1704-1. The cluster 1704-1 comprises acorresponding instance 1750-1 of a distributed WWH catalog. Althoughonly a single cluster and corresponding WWH catalog instance is shown inthis figure, it is assumed that similar instances of the distributed WWHcatalog are implemented in respective ones of the other clusters of themulti-cluster distributed data processing platform. The clusters arefurther assumed to be associated with respective distinct data zones,with each of the clusters being configured to perform processingoperations utilizing local data resources locally accessible within itscorresponding data zone. The WWH catalog instance 1750-1 of cluster1704-1 in combination with additional instances implemented forrespective additional ones of the clusters collectively provide adistributed WWH catalog service with capability to resolve local orremote status of data resources in the data zones of each of theclusters responsive to requests from any other one of the clusters.

The WWH catalog instance 1750-1 of the cluster 1704-1 comprises abrowsing API 1752-1 accessible to a plurality of clients includingclients 1712-1 and 1712-2, and a resolving API 1754-1 accessible to oneor more application master components of respective applications. Theresolving API 1754-1 is also accessible to the browsing API 1752-1, andvice-versa, as indicated by the bidirectional connection between them inthe figure.

The application master components in this embodiment more particularlycomprise respective WAM components denoted WAM₁ and WAM₂. Each of theseWAM components is assumed to be a YARN application master of acorresponding application running in the cluster 1704-1.

By way of example, a given one of the WAM components is illustrativelyconfigured to access the resolving API 1754-1 of the WWH cataloginstance 1750-1 of cluster 1704-1 in order to determine for each of aplurality of data resources to be utilized by the associated applicationwhether the data resource is a local data resource or a remote dataresource relative to cluster 1704-1. The WWH catalog instance 1750-1receives requests via its resolving API 1754-1 from the WAM componentsto identify for each of a plurality of data resources to be utilized bya corresponding application initiated in the cluster 1704-1 whether thedata resource is a local data resource or a remote data resourcerelative to that cluster. The WWH catalog instance 1750-1 providesresponses to those requests back to the requesting WAM components.

In the FIG. 17 embodiment, it is assumed that the distributed WWHcatalog is implemented as a plurality of WWH catalog instancesdistributed over the clusters with each of the clusters havingvisibility of only its corresponding one of the instances of thedistributed WWH catalog. The WWH catalog in such an arrangement andother similar arrangements herein is more generally referred to as a“distributed catalog service” of the corresponding multi-clusterdistributed data processing platform.

It is further assumed that the instances of the distributed WWH catalogare implemented as respective YARN applications running on respectiveones of the clusters. A given one of the instances of the distributedWWH catalog may be configured in accordance with a configuration filethat is stored in a predetermined storage location of the correspondingcluster, such as, for example, a predefined location in an underlyingHDFS of that cluster. The configuration file contains information aboutthe local and remote data resources having respective meta-resourcesthat are known to the corresponding instance of the WWH catalog. TheYARN application implementing a given instance of the distributed WWHcatalog is illustratively executed as part of a setup process for thecorresponding cluster.

In order to deploy the WWH catalog instance on a given cluster, aspecial job may be submitted to that cluster. For example, aWWHCatalogSubmit job may be used in order to submit a WWH cataloginstance into a cluster. The submitted job may contain a pre-resolvedmeta-resource pointing to one or more configuration files of respectivecatalogs that are to be created using this job.

In other embodiments, the configuration file may be replaced withanother type of configuration object. The term “configuration object” asused herein is intended to be broadly construed so as to encompass aconfiguration file or other type of stored configuration informationrelating to a distributed catalog instance.

The distributed WWH catalog is assumed in the present embodiment to be aprivate component of the WWH platform, and is therefore not subject tomodification by framework developers. Instead, only platform developersare permitted to modify the distributed WWH catalog in this embodiment.

As mentioned previously, a given WWH catalog instance such as WWHcatalog instance 1750-1 on cluster 1704-1 illustratively comprises aplurality of entries with each such entry comprising a meta-resourcecomprising information characterizing location and accessibility of acorresponding one of the data resources. The resolving API 1754-1illustratively returns a given meta-resource responsive to a requestthat includes a corresponding meta-resource identifier.

If a meta-resource identifier presented to WWH catalog instance 1750-1on cluster 1704-1 resolves to a local data resource of that cluster, theresolving API 1754-1 returns the corresponding meta-resource allowingthe requesting application to access the corresponding local dataresource in cluster 1704-1.

If a meta-resource identifier presented to WWH catalog instance 1750-1on cluster 1704-1 resolves to a remote data resource not locallyaccessible within that cluster, the resolving API 1754-1 can operate inone of a number of different evaluation modes. For example, in a “lazy”mode of evaluation, the resolving API 1754-1 returns information thatallows the application to access the remote instance of the catalog inorder to obtain the remote meta-resource. The returned information maybe in the form of a URL for the particular remote instance of thedistributed WWH catalog that is implemented in the remote cluster havinglocal access to the resource in question. Alternatively, the resolvingAPI 1754-1 can operate in an “eager” mode of evaluation in which itrequests the remote meta-resource from the WWH catalog instance in theremote cluster and then provides the received remote meta-resource tothe requesting application. This illustratively involves the resolvingAPI 1754-1 making one or more RPCs to other WWH catalog instances inother clusters.

If a particular meta-resource identifier is not found in the WWH cataloginstance 1750-1, the resolving API 1754-1 can return an error indicatingthat the corresponding meta-resource was not found. Alternatively, itcan call a Find API that searches for the meta-resource. The Find APImay go through a list of clusters that it knows and then, for each, itcalls the non-lazy mode of evaluation of the resolving API. It isassumed that the Find API has access to one or more lists of clusters.

The above-noted lazy evaluation mode is the default mode for theresolving API in some embodiments. For example, this evaluation mode isparticularly well-suited for embodiments in which meta-resourceidentifiers for remote resources are passed from a localWWH-ClusterNodeManager to a remote WWH-ClusterNodeManager in thatcluster, for resolving in the remote cluster. Such an arrangement isparticularly efficient in that it allows the final resolution of eachdata resource to be made in its local cluster.

A given one of the instances of the distributed WWH catalog such as WWHcatalog instance 1750-1 of cluster 1704-1 in conjunction with itsinitiation as a YARN application may be registered as a service with aservice registry of a resource manager of the cluster 1704-1. In such anarrangement, the service registry of the resource manager of the cluster1704-1 is utilized to identify the browsing and resolving APIs 1752-1and 1754-1 to requesting clients or WAM components.

FIG. 18 illustrates a method of utilizing a WWH catalog in anillustrative embodiment. In this embodiment, a portion 1800 of amulti-cluster distributed data processing platform comprises a firstYARN cluster 1804-1. The cluster 1804-1 comprises a correspondinginstance 1850-1 of a distributed WWH catalog. The WWH catalog instance1850-1 of the cluster 1804-1 comprises a browsing API 1852-1 accessibleto a client 1812-1. The WWH catalog instance 1850-1 further comprises aresolving API 1854-1 accessible to a WAM component of a correspondingapplication running in the cluster 1804-1. The features, arrangement andoperation of the WWH catalog instance 1850-1 are generally similar tothose of WWH catalog instance 1750-1 as previously described inconjunction with FIG. 17.

The method as illustrated in FIG. 18 includes a sequence of processingsteps indicated by circled numbers.

In step 1, the client 1812-1 browses the WWH catalog instance 1850-1 ofcluster 1804-1 via the browsing API 1852-1. As noted above, the WWHcatalog instance may register itself as a service with the YARN resourcemanager under an address such as services/wwh/catalog. The client 1812-1can therefore locate the browsing API 1852-1 of the WWH catalog instance1850-1 of the cluster 1804-1 by querying the resource manager registryservice of that cluster. The WWH catalog instance 1850-1 illustrativelyincludes lists of meta-resources with each such meta-resource having acorresponding meta-resource identifier and containing informationregarding location and accessibility of a corresponding data resource.Such lists are assumed to be provided in human-readable form to clientsvia the browsing API 1852-1.

In step 2, the client 1812-1 creates a processing job, illustratively anapplication utilizing a WWH processing framework, for submission to thecluster 1804-1. The processing job is configured to utilize dataresources having respective meta-resource identifiers from the WWHcatalog instance 1850-1.

In step 3, the client 1812-1 submits the job to the cluster 1804-1. Thesubmitted job includes a list of meta-resource identifiers forrespective data resources to be utilized in conjunction with executionof that job. The meta-resource identifiers are determined from the WWHcatalog instance based at least in part on the browsing in step 1.

In step 4, the WAM component created by YARN for the submitted jobaccesses the resolving API 1854-1 in order to resolve the local orremote status of the various data resources required for execution ofthe job. For example, the WAM component will attempt to resolve thelocal or remote status for all the meta-resource identifiers submittedwith the job to be executed. If a given meta-resource identifier isresolved to a remote data resource, a recursive job on the correspondingremote cluster will be initiated via a new remote cluster node managerof the WAM component.

The process will then continue in a manner similar to that previouslydescribed herein until the job is completed utilizing the cluster 1804-1to process data resources local to that cluster and one or moreadditional clusters to process remote data resources. The correspondingprocessing results are aggregated by one or more WWH aggregators andreturned to the client 1812-1.

FIGS. 19 through 24 illustrate example WWH catalog related features andfunctionality of illustrative embodiments. The circled numbers shown inFIGS. 21, 22, 23 and 24 are indicative of example processing sequenceflows utilized in these illustrative embodiments.

With reference to FIG. 19, an illustration of the recursive nature of ameta-resource of a WWH catalog is shown. In this embodiment, ameta-resource denoted/emp can provide access to multiple versions of theunderlying data resource using various additional or alternative dataformats, including XML, SQL and CSV formats.

FIG. 20 illustrates an example of supported services of the WWH catalogin one embodiment. In this embodiment, a client services interface ofWWH catalog metadata services supports a variety of requests such asquery, add an entry, delete an entry and update an entry. The WWHcatalog metadata services includes components such as a WWH CatalogManager and a WWH Catalog Master, as well as a network servicesinterface. The WWH catalog metadata services further support privacyand/or security services, and includes a capability to add futureservices.

Referring now to FIGS. 21-23, example techniques for resolving ameta-resource list are shown. It is assumed for this embodiment that amulti-cluster distributed data processing platform comprises sevendistinct YARN clusters in respective different geographic regions,namely, a Global cluster (“Cluster-Global”), an EMEA cluster(“Cluster-EMEA”), an APJ cluster (“Cluster-APJ”), an Americas cluster(“Cluster-Americas”), a Europe cluster (“Cluster-Europe”), a Middle Eastcluster (“Cluster-Middle-East”), and an Africa cluster(“Cluster-Africa”), where Global denotes a geographic regionencompassing all the other regions, EMEA denotes a geographic regionencompassing Europe, Middle East and Africa, APJ denotes a geographicregion encompassing Asia Pacific and Japan, Americas denotes ageographic region encompassing North and South America, Europe denotes ageographic region encompassing all of the countries in Europe, MiddleEast denotes a geographical region encompassing all of the countries inthe Middle East, and Africa denotes a geographical region encompassingall of the countries in Africa.

A WWH application master of the Global cluster submits a metadataresource list to the WWH Catalog Master, which identifies resources inthe EMEA cluster, the APJ cluster and the Americas cluster. FIGS. 21, 22and 23 illustrate interactions between WWH cluster node managers (“WWHCluster Node Managers”), and resource managers (“Resource Managers”)under the control of a WWH application master in resolving ameta-resource list denoted Genome/List. In this illustrative example,the technique for resolving a meta-resource list is implemented usingthe WWH framework.

Referring to FIG. 21 in particular, the WWH Application Master sends aresolve request (1) to the WWH Catalog Master passing the name of ameta-resource, the Genome/List illustrating, in this embodiment, a listof all genome files located worldwide, and passing a set of credentialsto be used for Global Access. The WWH Application Master then receivesfrom the WWH Catalog Master a list of resources that can be accessed byCluster-EMEA, by Cluster-APJ, and by Cluster-Americas. The WWHApplication Master then passes this information to each one of the WWHCluster Node Managers that will be responsible for the communicationwith the respective clusters. More specifically, in this embodiment, itwill pass the list of meta-resources Genome/EMEA/List and the associatedcredentials to access data in Cluster-EMEA to the WWH Cluster NodeManager that will communicate with Cluster-EMEA (2). It will then passthe list of meta-resources Genome/APJ/List and the associatedcredentials to access data in Cluster-APJ to the WWH Cluster NodeManager that will communicate with Cluster-APJ (3). In addition, it willpass the list of meta-resources Genome/Americas/List and the associatedcredentials to access data in Cluster-Americas to the WWH Cluster NodeManager that will communicate with Cluster-Americas (4).

With respect to FIG. 22, the embodiment illustrates the passing ofparameters between the WWH Cluster Node Managers and the ResourceManagers of the respective clusters with which they communicate. Aspreviously described in conjunction with FIG. 21, the WWH ApplicationMaster sends a Resolve request to the WWH Catalog Master (1). The WWHCatalog Master then returns respective lists of resources residing inEMEA-Cluster, APJ-Cluster and Americas-Cluster. The WWH ApplicationMaster then passes the separate lists and the associated credentials tothe respective WWH Cluster Node Managers (2, 3 and 4), which communicatewith the Resource Managers of the respective clusters.

With respect to FIG. 23, this embodiment illustrates the recursivenature of the approach, where a sequence of activities similar to thatpreviously described also occurs in Cluster-EMEA, once the applicationis submitted there. The Resource Manager in Cluster-EMEA creates a WWHApplication Master (1). The WWH Application Master then sends a Resolverequest to the WWH Catalog Master (2). The WWH Catalog Master thenreturns a list of resources residing in Cluster-Europe,Cluster-Middle-East and Cluster-Africa. The WWH Application Master thenpasses the separate lists and the associated credentials to therespective WWH Cluster Node Managers (3, 4 and 5).

FIG. 24 illustrates the manner in which the WWH-ApplicationMasterinitiates the WWH catalog in this embodiment. The arrangement isotherwise similar to that previously described in conjunction with FIG.12.

Again, the particular WWH components and their illustrative arrangementsand interactions as shown in FIGS. 19 through 24 is by way of exampleonly, and should not be construed as limiting in any way. Numerousalternative arrangements of components configured to interact indifferent manners can be used in alternative implementations of WWHplatforms of the type disclosed herein.

An example global MapReduce WWH framework and associated applicationflow utilizing the above-described WWH platform and associated WWHcatalog will now be described in more detail. In this example, the WWHframework more particularly comprises the above-notedWWH-MapReduce-GlobalReduce framework. It is assumed that a clientsubmits a WWH-MapReduce-GlobalReduce application for execution inaccordance with the corresponding framework. Each of the YARN clustersin the multi-cluster distributed data processing platform in thisembodiment runs a local MapReduce application. The output of allclusters is transmitted to a selected cluster and then that selectedcluster runs a global MapReduce application.

It is assumed that the local cluster that receives theWWH-MapReduce-GlobalReduce application from the submitting client isdenoted as cluster C0, and that there are two additional participatingclusters denoted as clusters C1 and C2, respectively. It is furtherassumed that these clusters are in respective separate data zones andthat each of the clusters has access to the local data resources of itscorresponding data zone.

The clusters C0, C1 and C2 in this example are implemented as respectiveDocker-based clusters, each running YARN and HDFS. Each cluster runs aninstance of a distributed WWH catalog as a YARN application. Thedifferent WWH catalog instances are differentiated by their respectiveconfiguration files. More particularly, each WWH catalog instance has aunique configuration file that describes the local and remotemeta-resources relative to the corresponding cluster. The localmeta-resources are assumed to be described by information identifyingtheir location in the local file system (e.g., file name or file path),and the remote meta-resources are assumed to be described by informationidentifying their respective remote clusters. Other types of informationindicative of location and accessibility of local or remote dataresources can be used in other embodiments.

The client submits the WWH-MapReduce-GlobalReduce application as a YARNapplication to the ResourceManager that resides on C0. A correspondingWWH-ApplicationMaster is started in conjunction with the submission ofthe WWH-MapReduce-GlobalReduce application. TheWWH-MapReduce-GlobalReduce application includes a list of meta-resourceentries from the WWH catalog, an aggregator class, and mapper, reducerand global-reducer classes.

It should be noted in this regard that the aggregator class is suppliedby the framework developer as part of the WWH-MapReduce-GlobalReduceframework. The client supplies the application-specific classes ofmapper, reducer and global-reducer, as well as the list of meta-resourceidentifiers from the WWH catalog which collectively represent input datafor the application.

The above-noted WWH-ApplicationMaster is created by the YARN resourcemanager of the cluster C0 upon submission of theWWH-MapReduce-GlobalReduce application. The WWH-ApplicationMasterutilizes the resolving API of the WWH catalog instance of cluster C0 toresolve the local or remote status of each of the meta-resourceidentifiers submitted with the application.

If a given meta-resource identifier is determined to represent a remotedata resource not accessible in cluster C0 but accessible in one of theother clusters C1 or C2, the WWH-ApplicationMaster will initiate arecursive job at the appropriate remote cluster via a corresponding oneof a plurality of WWH-ClusterNodeManagers configured to communicate withrespective ones of the remote clusters C1 and C2.

For those meta-resource identifiers that resolve to local data resourcesof cluster C0, a local MapReduce job will be executed on cluster C0using those resources via a local WWH-ClusterNodeManager.

When the WWH-ClusterNodeManager in C0 starts it examines the receivedjob and requests from the ResourceManager in C0 a new container thatwill run the supplied aggregator class. After the ResourceManager hasallocated the container, the WWH-ClusterNodeManager sends the jobinformation bundled with the WWH-ApplicationMaster information to theWWH-Aggregator as its initializing arguments. The WWH-Aggregator thenstarts and submits both local and remote jobs. When the WWH-Aggregatorstarts, for every cluster in the provided resources list, it collectsthe names of all the files for that particular cluster. It requests anew job execution on the appropriate cluster, with the same aggregator,mapper and reducer classes.

The WWH-ApplicationMaster receives the jobs submitted by theWWH-Aggregator. Any such job that is local is passed to the localWWH-ClusterNodeManager that was already created. For a remote job, aremote WWH-ClusterNodeManager is created. Assume that theWWH-ApplicationMaster examines a given job and sees that it is a remotejob to be assigned to C1. If it sees that there is no runningWWH-ClusterNodeManager for C1, the WWH-ApplicationMaster starts one,denoted WWH-ClusterNodeManager-C0-C1, and passes the job to it.

When WWH-ClusterNodeManager-C0-C1 starts it examines the job it receivedand determines that it is a remote job. It then acts just like aninitializing client. More particularly, WWH-ClusterNodeManager-C0-C1submits the WWH-ApplicationMaster to the ResourceManager of C1. Once theWWH-ApplicationMaster is up, WWH-ClusterNodeManager-C0-C1 submits a jobwith the same parameters, except for the resources, which are theresources only relevant to C1. When the WWH-ApplicationMaster on C1receives this job submission request it will recursively perform stepssimilar to those described above for the WWH-ApplicationMaster on C0.

When a WWH-Aggregator starts on a given cluster Ci, it receives the jobinformation which contains the list of files, a mapper class and areducer class. It then executes the job on its local cluster Ci usingregular YARN services. When the job completes it reports its results andterminates.

Local and remote results generated by respective local and remoteclusters are updated as follows. When the WWH-ApplicationMaster on agiven cluster Ci receives a job results link it looks up theWWH-ClusterNodeManager that is responsible for sending this job (e.g.,WWH-ClusterNodeManager-Cj-Ci), and passes the results to it. TheWWH-ClusterNodeManager-Cj-Ci then updates the job status.

The local and remote results are aggregated in the following manner. AWWH-Aggregator-For-MapReduce-Global in conjunction with monitoring thestatus of the various jobs will receive links to the results generatedby all the WWH-Aggregator-For-MapReduce-Local processes. Each time sucha link is received, the WWH-Aggregator-For-MapReduce-Global willdownload the results data to its local cluster. The data is transferredvia HTTP or other suitable protocols, and access control mechanisms maybe utilized in conjunction with such transfer. When all the jobs arecompleted and their results are fully downloaded, the WWH-Aggregator onC0 will execute the aggregation code, in this case the global reduce onC0. Upon completion of the aggregation, the WWH-Aggregator will post thelink for the results, just like any other WWH-Aggregator, and thenterminate itself. The submitting client will then be able to obtain theaggregated processing results.

As a more particular example of a WWH application that can utilize theabove-described WWH-MapReduce-GlobalReduce framework, consider aninformation processing system comprising multiple data centers locatedat different sites around the world, with the data centers maintainingrespective large local document repositories. Data analysts wish toperform analytics in the form of a simple word count on the documents onall the sites. However, in performing this analysis, data centers cannottransmit complete documents to one another, but only the results oftheir respective local word counts. This restriction can be the resultof a privacy issue (e.g., the data centers do not wish to expose theirdocuments to the public), network bandwidth (e.g., the data is simplytoo large), or both.

A WWH application for performing a global word count in theabove-described system can be configured as follows. First, a localword-count will be performed on each of the YARN clusters utilizing thelocal MapReduce framework. Then, the results of the local MapReduceprocessing are transmitted to a single one of the clusters, and a globalreducing task is performed on the processing results in that singlecluster. This last operation is illustratively performed by thepreviously-described global reducer which is part of theWWH-MapReduce-GlobalReduce framework. In other embodiments, alternativeaggregation techniques can be used in place of the global reducer at asingle cluster. For example, processing results can be aggregatedincrementally using multiple ones of the clusters.

A wide variety of other types of analytics processing can be implementedusing WWH platforms as disclosed herein.

As another example, bioinformatics applications for metagenomics-basedbiological surveillance can utilize the WWH-MapReduce-GlobalReduceframework. In one such arrangement, an initial cluster accepts samplegenomes which are sent to a plurality of other clusters. Each of theclusters uses a local MapReduce process to compare the samples withprivate genomic information locally accessible in the correspondingcluster. The results of this local comparison in each cluster are in theform of one or more vectors which are sent back to the initial cluster.The initial cluster then runs a global reducer on the received vectorscreating aggregated processing results in the form of a results matrix.This results matrix may be sent to the client for further analysis inorder to detect the particular sample causing the problem.

In some embodiments configured to implement bioinformatics applicationsof the type described above, reads of local biological samples obtainedfrom metagenomics sequencing are subject to mapping operations in eachof the clusters. For example, one or more reads of a given biologicalsample may be subject to mapping based on string resemblance to targetgenomic sequences. Such a mapping arrangement is illustratively used togenerate a hit abundance score vector for the given biological sample.Multiple such hit abundance score vectors generated for differentbiological samples are combined into a hit abundance score matrix thatis utilized in characterizing a disease, infection or contamination, orotherwise providing analytics functionality within the system.

Yet another example is a cooperative security anomaly detectionapplication which uses accumulating evidence to improve the quality oflocal detectors. Each local detector is run on a single YARN cluster ofa multi-cluster WWH platform, and uses its own detecting algorithmimplemented as a local MapReduce application using its own private data.The aggregated results of the detection are sent back to the initialcluster using aggregated non-private features only. The initial clusterexecutes a global reducer to select a set of the best global featuresand these are sent back to the local detectors of the respectiveclusters. This process continues for several iterations, with eachiteration comprising a new global map-reduce application instance, untilit converges. The process considerably improves local detector accuracyusing the detection results received from the other clusters.

An arrangement of this type can be implemented in a system for malwaredetection that operates by analyzing Big Data comprising Domain NameService (DNS) transactions associated with the web site of a largecompany. Clearly, such a company will be reluctant to share itstransactions logs with other businesses. However, the company may wellbe willing to share anonymized statistical data in order to defeat amalware threat. By sharing statistical data of multiple sites in themanner described above, an improved malware detector can be constructed.Such a shared detector can use a multi-cluster distributed dataprocessing platform of the type disclosed herein in order to enable therun of the improved detector on data in multiple sites, each using thedetector on its own transaction logs and improving the probability ofmalware detection. No sharing of data and no common file system isneeded or used. Other embodiments can incorporate additionalfunctionality for access control, progress monitoring and support of apluggable failure handling policy.

These example applications demonstrate the use of theWWH-MapReduce-GlobalReduce framework, and serve to illustrate theflexibility provided by the distributed WWH catalog in terms of locatingrelevant input data. They also demonstrate the privacy and performancefeatures of WWH platforms.

Again, the use of MapReduce as part of a WWH framework is by way ofillustrative example only. Numerous alternative frameworks can beutilized as part of a given WWH framework, including in some embodimentsany framework supported by YARN, as well as other frameworks in non-YARNembodiments.

The multi-cluster distributed data processing platforms of illustrativeembodiments disclosed herein provide significant advantages relative toconventional arrangements.

As mentioned previously, illustrative embodiments move the computationinstead of moving the data and create an abstraction to distributed BigData in order to overcome the drawbacks of conventional systems,providing significant advantages in terms of both performance andprivacy, and related advantages such as the facilitation of GRC, asoutlined in detail elsewhere herein.

Additional illustrative embodiments comprising beacon-based arrangementswill now be described with references to FIGS. 25 through 28. In theseembodiments, it is assumed that a beacon-based distributed dataprocessing platform comprises a plurality of beacon lit sites. Suchsites may comprise, for example, respective geographically-distributeddata centers or other repositories of locally-accessible data to beprocessed by WWH nodes or other processing nodes of the platform.

It is further assumed that the beacons generally correspond torespective beacons configured in accordance with the Beacon Project ofthe Global Alliance for Genome and Health (GA4GH), but suitably modifiedto support WWH functionality as disclosed herein. The beacons maytherefore be implemented at least in part in a manner analogous to GA4GHbeacons, although a wide variety of other types of beacons can be usedin other embodiments. The term “beacon” as used herein is intended to bebroadly construed so as to encompass various mechanisms in which a givensite can make its presence and availability known to processing nodes ofa distributed data processing platform. It is possible that a given sitemay itself comprise a YARN cluster or at least one WWH node in someembodiments.

Referring now to FIG. 25, a client 2512-1 of a WWH platform comprises aYARN API 2500-1. The YARN API 2500-1 is advantageously configured toleverage the WWH functionality of the WWH platform. In this embodiment,it is assumed that the client 2512-1 receives as one of its inputs alist of beacon “lit” sites, where such a site is assumed to have itsbeacon activated or “lit.” The client also receives a beacon query,which illustratively comprises a request for information or analysisinvolving one or more of the beacon lit sites on the list of beacon litsites, and generates one or more answers in response to the beaconquery, utilizing the WWH platform to access one or more of the beaconlit sites and their respective sets of locally-available data resources.

The beacon lit sites are examples of what are more generally referred toherein as “beacon entities.” Such entities generally comprise respectiveactivatable beacons, and may represent respective participants in abeacon network.

FIG. 26 shows a WWH platform 2600 in an illustrative embodiment thatincludes the client 2512-1 and its YARN API 2500-1. The WWH platform2600 in this embodiment further comprises WWH nodes 2602-1, 2602-2, . .. 2602-k, . . . 2602-K, each comprising a YARN component, a WWHcomponent and a MapReduce (“MR”) component. The YARN components of therespective WWH nodes 2602 include respective resource managers eachdenoted RM. The WWH platform 2600 further comprises beacon lit sites2604-1, 2604-2, . . . 2604-k, . . . 2604-K having local access torespective sets 2610-1, 2610-2, . . . 2610-k, . . . 2610-K of dataresources, with each data resource being denoted R. Each of the WWHnodes 2602 has the capability of establishing a possible connection toat least one of the beacon lit sites 2604, with the connection beingillustrated by a dashed line in the figure.

As mentioned previously, values of variables such as K used herein arearbitrary, and can vary from embodiment to embodiment. For example,other embodiments of the WWH platform can include different numbers ofWWH nodes, beacon lit sites and associated sets of data resources.

In the FIG. 26 embodiment, the client 2512-1 via its YARN API 2500-1becomes a client of the YARN component of the first WWH node 2602-1.More particularly, the YARN API 2500-1 accesses the YARN component ofthe first WWH node 2602-1 via the RM of the YARN component of that WWHnode. The WWH component of the first WWH node 2602-1 leverages at leasta subset of the other WWH nodes 2602 via their respective RMs withintheir respective YARN components. This allows computations or otheroperations associated with the beacon query to be performed in adistributed manner under the control of the WWH nodes 2602 that areclosest to or have another type of association or relationship with therelevant beacon lit sites. Accordingly, in this embodiment and othersimilar embodiments, one or more additional WWH nodes are selected by agiven one of the WWH nodes for handling at least portions of the beaconquery based at least in part on proximity of the one or more additionalWWH nodes to a corresponding one of the beacon entities. Again,associations or relationships other than or in addition to proximity canbe used in selecting a particular WWH node for participation inprocessing of the beacon query.

Such an arrangement provides significant advantages relative toalternative beacon arrangements in which the client would otherwise haveto interact directly with each of the beacon lit sites in order toresolve a given beacon query. In the present embodiment, the client doesnot need to know which beacon-based resources can be accessed and wheresuch resources are located within the system.

Moreover, the WWH platform 2600 can not only execute beacon queries butcan more generally perform any other types of computations or analyticsprocessing operations in accordance with other frameworks supported byYARN, such as MapReduce, Spark and many others. These operations areadvantageously performed in decentralized and privacy-preserving mannerwithin the WWH platform.

Although only a single layer of WWH nodes 2602 is shown in thisembodiment, other embodiments can include multiple distinct layers ofWWH nodes.

It should also be noted that this embodiment and other beacon-baseddistributed data processing platform embodiments illustratively operateusing a recursive approach similar to that described in the context ofother WWH platform embodiments herein. For example, one WWH node candirectly access those beacon lit sites that it has local access to whilealso initiating one or more applications on one or more other WWH nodesto obtain remote access to one or more other beacon lit sites. Also,advantages similar to those of the other WWH platform embodiments interms of system performance and compliance with privacy, security andGRC requirements are obtained.

Another beacon-based distributed data processing platform embodiment isillustrated in FIGS. 27 and 28. This embodiment is similar to theembodiment previously described in conjunction with FIGS. 25 and 26, butadditionally makes use of WWH catalog functionality as part of the WWHplatform.

Referring now to FIG. 27, a client 2712-1 of a WWH platform comprises aYARN API 2700-1. The YARN API 2700-1 is advantageously configured toleverage the WWH functionality of the WWH platform. In this embodiment,it is assumed that the client 2712-1 receives as one of its inputs alist of WWH catalog entries. The client also receives a beacon query,which illustratively comprises a request for information or analysisinvolving one or more of the WWH catalog entries on the list of WWHcatalog entries, and generates one or more answers in response to thebeacon query, utilizing the WWH platform to access one or more of beaconlit sites and their respective sets of locally-available data resources.Accordingly, in this embodiment, the list of beacon lit sites isreplaced with the list of WWH catalog entries. Such an arrangementadvantageously avoids the need for client applications to have knowledgeof lists of beacon lit sites for use in processing a beacon query.

FIG. 28 shows a WWH platform 2800 in an illustrative embodiment thatincludes the client 2712-1 and its YARN API 2700-1. The WWH platform2800 in this embodiment further comprises WWH nodes 2802-1, 2802-2, . .. 2802-k, . . . 2802-K, each comprising a YARN component and a WWHcomponent. The YARN components of the respective WWH nodes 2802 includerespective resource managers each denoted RM. The WWH platform 2800further comprises beacon lit sites 2804-1, 2804-2, . . . 2804-k, . . .2804-K having local access to respective sets 2810-1, 2810-2, . . .2810-k, . . . 2810-K of data resources, with each data resource beingdenoted R. Each of the WWH nodes 2802 has the capability of establishinga possible connection to at least one of the beacon lit sites 2804, withthe connection being illustrated by a dashed line in the figure. Again,the particular numbers of WWH nodes, beacon lit sites and associatedsets of data resources are arbitrary.

In the FIG. 28 embodiment, the client 2712-1 via its YARN API 2700-1becomes a client of the YARN component of the first WWH node 2802-1.More particularly, the YARN API 2700-1 accesses the YARN component ofthe first WWH node 2802-1 via the RM of the YARN component of that WWHnode. The WWH component of the first WWH node 2802-1 leverages at leasta subset of the other WWH nodes 2802 via their respective RMs withintheir respective YARN components. This allows computations or otheroperations associated with the beacon query to be performed in adistributed manner under the control of the WWH nodes 2802 that areclosest to or have another type of association or relationship with therelevant beacon lit sites to be contacted in conjunction with processingof the beacon query.

Within each of the WWH nodes 2802 in this embodiment, the YARN RMinitiates a WWH Application Master as illustrated. The WWH ApplicationMasters interact with respective WWH Catalog Masters, which representrespective instances of a distributed WWH catalog service in thisembodiment.

By way of example, the use of the distributed WWH catalog service inthis embodiment allows the client to identify a particular subset ofbeacon lit sites that should participate in execution of a given beaconquery. This is illustratively only a relatively small but focused subsetof the full set of beacon lit sites. Accordingly, the distributed WWHcatalog functionality of the FIG. 28 embodiment will tend to reduce theamount of network traffic and processing overhead associated withexecution of a given beacon query.

Like the embodiment described in conjunction with FIGS. 25 and 26, theembodiment described in conjunction with FIGS. 27 and 28 also providessignificant additional advantages relative to alternative beaconarrangements in which the client would otherwise have to interactdirectly with each of the beacon lit sites in order to resolve a givenbeacon query. Moreover, the WWH platform 2800 can not only executebeacon queries but can more generally perform any other types ofcomputations or analytics processing operations in accordance with otherframeworks supported by YARN, such as MapReduce, Spark and many others.These operations are advantageously performed in decentralized andprivacy-preserving manner within the WWH platform 2800. In addition,although the WWH platform 2800 is shown as comprising a single layer ofWWH nodes 2802 in this embodiment, other embodiments can includemultiple distinct layers of WWH nodes.

The beacon-based distributed data processing platforms described aboveprovide enhanced processing arrangements for use in the GA4GH BeaconProject, as well as in numerous other contexts involving use of beacons.For example, by using WWH as the computing paradigm for the BeaconProject, the resulting system becomes far more extensible thanclient-based arrangements and it can leverage all of the frameworkssupported by YARN, allowing much more sophisticated computations andother analytics operations to be performed using data resources ofbeacon lit sites. Moreover, it allows the analytics to be performed in amore focused and distributed manner that relieves the client of havingto communicate directly with each of a relatively large number of beaconlit sites.

The WWH catalog can be used in such embodiments to store metadataregarding the participants in a network of beacon lit sites, therebyallowing for query optimization based on particular beacon lit sites.For example, such metadata can be used to determine which of the beaconnetwork participants should be part of the execution of a given query.The WWH catalog can allow for the creation of multiple distinct virtualbeacon networks, each comprising a different subset of beacon networkparticipants, with particular types of queries being sent only tocertain virtual beacon networks.

It is to be appreciated that the particular types of system features andfunctionality as illustrated in the drawings and described above areexemplary only, and numerous other arrangements may be used in otherembodiments.

It was noted above that portions of an information processing system asdisclosed herein may be implemented using one or more processingplatforms. Illustrative embodiments of such platforms will now bedescribed in greater detail. These and other processing platforms may beused to implement at least portions of other information processingsystems in other embodiments of the invention. A given such processingplatform comprises at least one processing device comprising a processorcoupled to a memory.

One illustrative embodiment of a processing platform that may be used toimplement at least a portion of an information processing systemcomprises cloud infrastructure including virtual machines implementedusing a hypervisor that runs on physical infrastructure. The cloudinfrastructure further comprises sets of applications running onrespective ones of the virtual machines under the control of thehypervisor. It is also possible to use multiple hypervisors eachproviding a set of virtual machines using at least one underlyingphysical machine. Different sets of virtual machines provided by one ormore hypervisors may be utilized in configuring multiple instances ofvarious components of the system.

These and other types of cloud infrastructure can be used to providewhat is also referred to herein as a multi-tenant environment. One ormore system components such as WWH nodes 102 and YARN clusters 104, orportions thereof, can be implemented as respective tenants of such amulti-tenant environment.

In some embodiments, the cloud infrastructure additionally oralternatively comprises a plurality of containers implemented usingcontainer host devices. For example, a given container of cloudinfrastructure illustratively comprises a Docker container or other typeof LXC. The containers may be associated with respective tenants of amulti-tenant environment of the system 100, although in otherembodiments a given tenant can have multiple containers. The containersmay be utilized to implement a variety of different types offunctionality within the system 100. For example, containers can be usedto implement respective cloud compute nodes or cloud storage nodes of acloud computing and storage system. The compute nodes or storage nodesmay be associated with respective cloud tenants of a multi-tenantenvironment of system 100. Containers may be used in combination withother virtualization infrastructure such as virtual machines implementedusing a hypervisor.

Another illustrative embodiment of a processing platform that may beused to implement at least a portion of an information processing systemcomprises a plurality of processing devices which communicate with oneanother over at least one network. The network may comprise any type ofnetwork, including by way of example a global computer network such asthe Internet, a WAN, a LAN, a satellite network, a telephone or cablenetwork, a cellular network, a wireless network such as a WiFi or WiMAXnetwork, or various portions or combinations of these and other types ofnetworks.

As mentioned previously, some networks utilized in a given embodimentmay comprise high-speed local networks in which associated processingdevices communicate with one another utilizing PCIe cards of thosedevices, and networking protocols such as InfiniBand, Gigabit Ethernetor Fibre Channel.

Each processing device of the processing platform comprises a processorcoupled to a memory. The processor may comprise a microprocessor, amicrocontroller, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA) or other type of processingcircuitry, as well as portions or combinations of such circuitryelements. The memory may comprise random access memory (RAM), read-onlymemory (ROM) or other types of memory, in any combination. The memoryand other memories disclosed herein should be viewed as illustrativeexamples of what are more generally referred to as “processor-readablestorage media” storing executable program code of one or more softwareprograms.

Articles of manufacture comprising such processor-readable storage mediaare considered embodiments of the present invention. A given sucharticle of manufacture may comprise, for example, a storage array, astorage disk or an integrated circuit containing RAM, ROM or otherelectronic memory, or any of a wide variety of other types of computerprogram products. The term “article of manufacture” as used hereinshould be understood to exclude transitory, propagating signals.

Also included in the processing device is network interface circuitry,which is used to interface the processing device with the network andother system components, and may comprise conventional transceivers.

Again, these particular processing platforms are presented by way ofexample only, and other embodiments may include additional oralternative processing platforms, as well as numerous distinctprocessing platforms in any combination, with each such platformcomprising one or more computers, servers, storage devices or otherprocessing devices.

It should therefore be understood that in other embodiments differentarrangements of additional or alternative elements may be used. At leasta subset of these elements may be collectively implemented on a commonprocessing platform, or each such element may be implemented on aseparate processing platform.

Also, numerous other arrangements of computers, servers, storage devicesor other components are possible in an information processing system asdisclosed herein. Such components can communicate with other elements ofthe information processing system over any type of network or othercommunication media.

As indicated previously, components of an information processing systemas disclosed herein can be implemented at least in part in the form ofone or more software programs stored in memory and executed by aprocessor of a processing device. For example, at least portions of thefunctionality of a given YARN cluster or associated data processing nodein a particular embodiment are illustratively implemented in the form ofsoftware running on one or more processing devices.

It should again be emphasized that the above-described embodiments ofthe invention are presented for purposes of illustration only. Manyvariations and other alternative embodiments may be used. For example,the disclosed techniques are applicable to a wide variety of other typesof information processing systems, multi-cluster distributed dataprocessing platforms, application frameworks, processing nodes, localand remote data resources and other components. Also, the particularconfigurations of system and device elements, associated processingoperations and other functionality illustrated in the drawings can bevaried in other embodiments. Moreover, the various assumptions madeabove in the course of describing the illustrative embodiments shouldalso be viewed as exemplary rather than as requirements or limitationsof the invention. Numerous other alternative embodiments within thescope of the appended claims will be readily apparent to those skilledin the art.

What is claimed is:
 1. A method comprising: initiating a firstapplication in a first one of a plurality of distributed processingnodes, wherein the plurality of distributed processing nodes comprise aplurality of clusters associated with respective data zones, each of theclusters being configured to perform processing operations utilizinglocal data resources locally accessible within its corresponding datazone; determining a plurality of data resources to be utilized by atleast a portion of the first application, the plurality of dataresources comprising at least one data resource that is not locallyaccessible within a first data zone associated with the firstdistributed processing node; responsive to initiation of the firstapplication, identifying a plurality of beacon entities to be contactedto determine additional ones of the plurality of distributed processingnodes having associated data zones which include the at least one dataresource not locally accessible within the first data zone associatedwith the first distributed processing node; accessing a distributedcatalog service to identify at least a subset of the plurality of beaconentities to be contacted in conjunction with execution of at least aportion of the first application; for each of at least the subset of theidentified beacon entities, initiating an additional application in anadditional one of the plurality of distributed processing nodes, whereineach of the beacon entities is configured to perform processingoperations associated with at least one of the first and one or moreadditional applications utilizing data resources locally accessiblewithin a corresponding data zone; aggregating first and one or moreadditional processing results from the first and one or more additionalprocessing nodes, wherein the aggregated processing results at leastpartially conceal identifying information of the first and one or moreadditional processing nodes providing the first and one or moreadditional processing results; and providing the aggregated processingresults to a client; wherein the distributed catalog service isconfigured to store metadata characterizing the beacon entities; whereinthe beacon entities are configured for organization, based at least inpart on the stored metadata characterizing the beacon entities, into aplurality of virtual beacon networks for use in processing respectivedifferent types of beacon queries; and wherein the method is implementedby at least one processing device comprising a processor coupled to amemory.
 2. The method of claim 1 wherein the plurality of beaconentities comprise respective participants in a beacon network.
 3. Themethod of claim 1 wherein the plurality of beacon entities compriserespective geographically-distributed regional data centers eachconfigured to perform analytics processing utilizing locally accessibledata resources of a corresponding data zone.
 4. The method of claim 1wherein the beacon entities have respective activatable beacons.
 5. Themethod of claim 1 wherein the distributed catalog service is distributedover the plurality of processing nodes with each such processing nodehaving visibility of a corresponding distinct portion of the distributedcatalog based on its locally accessible data resources.
 6. The method ofclaim 1 wherein a given one of the applications initiated in acorresponding one of the processing nodes comprises an applicationmaster component.
 7. The method of claim 6 wherein the applicationmaster component is configured to access a resolving interface of adistributed catalog service.
 8. The method of claim 1 wherein at leastone of the additional ones of the plurality of distributed processingnodes is selected based at least in part on proximity of said at leastone additional processing node to a corresponding one of the beaconentities.
 9. The method of claim 1 wherein aggregating processingresults from the first and one or more additional processing nodespreserves at least one policy specifying: that processing resultsgenerated by a given one of the additional applications initiated in agiven one of the additional processing nodes utilizing data resourceslocally accessible within a given data zone are permitted to betransmitted to the first processing node; and that the data resourceslocally accessible within the given data zone associated with the givenadditional processing node utilized to obtain the transmitted processingresults are not permitted to be transmitted to the first processingnode.
 10. A computer program product comprising a non-transitoryprocessor-readable storage medium having stored therein program code ofone or more software programs; wherein the program code when executed byat least one processing device causes said at least one processingdevice: to initiate a first application in a first one of a plurality ofdistributed processing nodes, wherein the plurality of distributedprocessing nodes comprise a plurality of clusters associated withrespective data zones, each of the clusters being configured to performprocessing operations utilizing local data resources locally accessiblewithin its corresponding data zone; to determine a plurality of dataresources to be utilized by at least a portion of the first application;the plurality of data resources comprising at least one data resourcethat is not locally accessible within a first data zone associated withthe first distributed processing node; responsive to initiation of thefirst application, to identify a plurality of beacon entities to becontacted to determine additional ones of the plurality of distributedprocessing nodes having associated data zones which include the at leastone data resource not locally accessible within the first data zoneassociated with the first distributed processing node; to access adistributed catalog service to identify at least a subset of theplurality of beacon entities to be contacted in conjunction withexecution of at least a portion of the first application; for each of atleast the subset of the identified beacon entities, to initiate anadditional application in an additional one of the plurality ofdistributed processing nodes, wherein each of the beacon entities isconfigured to perform processing operations associated with at least oneof the first and one or more additional applications utilizing dataresources locally accessible within a corresponding data zone; toaggregate first and one or more additional processing results from thefirst and one or more additional processing nodes, wherein theaggregated processing results at least partially conceal identifyinginformation of the first and one or more additional processing nodesproviding the first and one or more additional processing results; andto provide the aggregated processing results to a client; wherein thedistributed catalog service is configured to store metadatacharacterizing the beacon entities; and wherein the beacon entities areconfigured for organization, based at least in part on the storedmetadata characterizing the beacon entities, into a plurality of virtualbeacon networks for use in processing respective different types ofbeacon queries.
 11. The computer program product of claim 10 wherein thedistributed catalog service is distributed over the plurality ofprocessing nodes with each such processing node having visibility of acorresponding distinct portion of the distributed catalog based on itslocally accessible data resources.
 12. The computer program product ofclaim 10 wherein the plurality of beacon entities comprise respectiveparticipants in a beacon network.
 13. The computer program product ofclaim 10 wherein the plurality of beacon entities comprise respectivegeographically-distributed regional data centers each configured toperform analytics processing utilizing locally accessible data resourcesof a corresponding data zone.
 14. The computer program product of claim10 wherein at least one of the additional ones of the plurality ofdistributed processing nodes is selected based at least in part onproximity of said at least one additional processing node to acorresponding one of the beacon entities.
 15. An apparatus comprising:at least one processing device having a processor coupled to a memory;wherein said at least one processing device is configured: to initiate afirst application in a first one of a plurality of distributedprocessing nodes, wherein the plurality of distributed processing nodescomprise a plurality of clusters associated with respective data zones,each of the clusters being configured to perform processing operationsutilizing local data resources locally accessible within itscorresponding data zone; to determine a plurality of data resources tobe utilized by at least a portion of the first application, theplurality of data resources comprising at least one data resource thatis not locally accessible within a first data zone associated with thefirst distributed processing node; responsive to initiation of the firstapplication, to identify a plurality of beacon entities to be contactedto determine additional ones of the plurality of distributed processingnodes having associated data zones which include the at least one dataresource not locally accessible within the first data zone associatedwith the first distributed processing node; to access a distributedcatalog service to identify at least a subset of the plurality of beaconentities to be contacted in conjunction with execution of at least aportion of the first application; for each of at least the subset of theidentified beacon entities, to initiate an additional application in anadditional one of the plurality of distributed processing nodes, whereineach of the beacon entities is configured to perform processingoperations associated with at least one of the first and one or moreadditional applications utilizing data resources locally accessiblewithin a corresponding data zone; to aggregate first and one or moreadditional processing results from the first and one or more additionalprocessing nodes, wherein the aggregated processing results at leastpartially conceal identifying information of the first and one or moreadditional processing nodes providing the first and one or moreadditional processing results; and to provide the aggregated processingresults to a client, wherein the distributed catalog service isconfigured to store metadata characterizing the beacon entities; andwherein the beacon entities are configured for organization, based atleast in part on the stored metadata characterizing the beacon entities,into a plurality of virtual beacon networks for use in processingrespective different types of beacon queries.
 16. The apparatus of claim15 wherein the distributed catalog service is distributed over theplurality of processing nodes with each such processing node havingvisibility of a corresponding distinct portion of the distributedcatalog based on its locally accessible data resources.
 17. Aninformation processing system comprising the plurality of distributedprocessing nodes of claim
 15. 18. The apparatus of claim 15 wherein theplurality of beacon entities comprise respective participants in abeacon network.
 19. The apparatus of claim 15 wherein the plurality ofbeacon entities comprise respective geographically-distributed regionaldata centers each configured to perform analytics processing utilizinglocally accessible data resources of a corresponding data zone.
 20. Theapparatus of claim 15 wherein at least one of the additional ones of theplurality of distributed processing nodes is selected based at least inpart on proximity of said at least one additional processing node to acorresponding one of the beacon entities.